[
{
"path": "LICENSE",
"content": "MIT License\n\nCopyright (c) 2025 chaoyi-wu\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
},
{
"path": "Quick_demo/Language_files/config.json",
"content": "{\n \"_name_or_path\": \"/home/cs/leijiayu/wuchaoyi/Finetune_LLAMA/LLAMA_Model/llama-13b-hf\",\n \"architectures\": [\n \"LlamaForCausalLM\"\n ],\n \"bos_token_id\": 0,\n \"eos_token_id\": 1,\n \"hidden_act\": \"silu\",\n \"hidden_size\": 5120,\n \"initializer_range\": 0.02,\n \"intermediate_size\": 13824,\n \"max_sequence_length\": 2048,\n \"model_type\": \"llama\",\n \"num_attention_heads\": 40,\n \"num_hidden_layers\": 40,\n \"pad_token_id\": -1,\n \"rms_norm_eps\": 1e-06,\n \"tie_word_embeddings\": false,\n \"torch_dtype\": \"float32\",\n \"transformers_version\": \"4.28.0.dev0\",\n \"use_cache\": true,\n \"vocab_size\": 32000\n}\n"
},
{
"path": "Quick_demo/Language_files/special_tokens_map.json",
"content": "{}"
},
{
"path": "Quick_demo/Language_files/tokenizer_config.json",
"content": "{\"bos_token\": \"\", \"eos_token\": \"\", \"model_max_length\": 1000000000000000019884624838656, \"tokenizer_class\": \"LlamaTokenizer\", \"unk_token\": \"\"}"
},
{
"path": "Quick_demo/MedKEBERT/config.json",
"content": "{\n \"_name_or_path\": \"xmcmic/Med-KEBERT\",\n \"architectures\": [\n \"BertModel\"\n ],\n \"attention_probs_dropout_prob\": 0.1,\n \"classifier_dropout\": null,\n \"gradient_checkpointing\": false,\n \"hidden_act\": \"gelu\",\n \"hidden_dropout_prob\": 0.1,\n \"hidden_size\": 768,\n \"initializer_range\": 0.02,\n \"intermediate_size\": 3072,\n \"layer_norm_eps\": 1e-12,\n \"max_position_embeddings\": 512,\n \"model_type\": \"bert\",\n \"num_attention_heads\": 12,\n \"num_hidden_layers\": 12,\n \"output_hidden_states\": true,\n \"pad_token_id\": 0,\n \"position_embedding_type\": \"absolute\",\n \"torch_dtype\": \"float32\",\n \"transformers_version\": \"4.24.0\",\n \"type_vocab_size\": 2,\n \"use_cache\": true,\n \"vocab_size\": 30522\n}\n"
},
{
"path": "Quick_demo/MedKEBERT/special_tokens_map.json",
"content": "{\n \"cls_token\": \"[CLS]\",\n \"mask_token\": \"[MASK]\",\n \"pad_token\": \"[PAD]\",\n \"sep_token\": \"[SEP]\",\n \"unk_token\": \"[UNK]\"\n}\n"
},
{
"path": "Quick_demo/MedKEBERT/tokenizer.json",
"content": "{\n \"version\": \"1.0\",\n \"truncation\": null,\n \"padding\": null,\n \"added_tokens\": [\n {\n \"id\": 0,\n \"content\": \"[PAD]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 1,\n \"content\": \"[UNK]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 2,\n \"content\": \"[CLS]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 3,\n \"content\": \"[SEP]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n },\n {\n \"id\": 4,\n \"content\": \"[MASK]\",\n \"single_word\": false,\n \"lstrip\": false,\n \"rstrip\": false,\n \"normalized\": false,\n \"special\": true\n }\n ],\n \"normalizer\": {\n \"type\": \"BertNormalizer\",\n \"clean_text\": true,\n \"handle_chinese_chars\": true,\n \"strip_accents\": null,\n \"lowercase\": true\n },\n \"pre_tokenizer\": {\n \"type\": \"BertPreTokenizer\"\n },\n \"post_processor\": {\n \"type\": \"TemplateProcessing\",\n \"single\": [\n {\n \"SpecialToken\": {\n \"id\": \"[CLS]\",\n \"type_id\": 0\n }\n },\n {\n \"Sequence\": {\n \"id\": \"A\",\n \"type_id\": 0\n }\n },\n {\n \"SpecialToken\": {\n \"id\": \"[SEP]\",\n \"type_id\": 0\n }\n }\n ],\n \"pair\": [\n {\n \"SpecialToken\": {\n \"id\": \"[CLS]\",\n \"type_id\": 0\n }\n },\n {\n \"Sequence\": {\n \"id\": \"A\",\n \"type_id\": 0\n }\n },\n {\n \"SpecialToken\": {\n \"id\": \"[SEP]\",\n \"type_id\": 0\n }\n },\n {\n \"Sequence\": {\n \"id\": \"B\",\n \"type_id\": 1\n }\n },\n {\n \"SpecialToken\": {\n \"id\": \"[SEP]\",\n \"type_id\": 1\n }\n }\n ],\n \"special_tokens\": {\n \"[CLS]\": {\n \"id\": \"[CLS]\",\n \"ids\": [\n 2\n ],\n \"tokens\": [\n \"[CLS]\"\n ]\n },\n \"[SEP]\": {\n \"id\": \"[SEP]\",\n \"ids\": [\n 3\n ],\n \"tokens\": [\n 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\"randomization\": 17807,\n \"##phos\": 17808,\n \"micronucle\": 17809,\n \"##obium\": 17810,\n \"##ilin\": 17811,\n \"junctional\": 17812,\n \"##lofen\": 17813,\n \"162\": 17814,\n \"impairs\": 17815,\n \"dimeric\": 17816,\n \"vor\": 17817,\n \"aggress\": 17818,\n \"##93\": 17819,\n \"doll\": 17820,\n \"bla\": 17821,\n \"gaining\": 17822,\n \"##sd\": 17823,\n \"##atre\": 17824,\n \"ultrastructure\": 17825,\n \"xi\": 17826,\n \"judgments\": 17827,\n \"nitroph\": 17828,\n \"factorial\": 17829,\n \"ral\": 17830,\n \"minus\": 17831,\n \"enkephalin\": 17832,\n \"##roma\": 17833,\n \"##ronch\": 17834,\n \"annealing\": 17835,\n \"##eller\": 17836,\n \"splenectomy\": 17837,\n \"xl\": 17838,\n \"178\": 17839,\n \"##onge\": 17840,\n \"##recogn\": 17841,\n \"transduced\": 17842,\n \"checkpoint\": 17843,\n \"priorities\": 17844,\n \"empower\": 17845,\n \"##olase\": 17846,\n \"leishmaniasis\": 17847,\n \"triton\": 17848,\n \"positional\": 17849,\n \"##oris\": 17850,\n \"arrested\": 17851,\n \"listeria\": 17852,\n \"gadolinium\": 17853,\n \"adipocyte\": 17854,\n \"##kinase\": 17855,\n \"crystallographic\": 17856,\n \"##oned\": 17857,\n \"neurole\": 17858,\n \"eruption\": 17859,\n \"prur\": 17860,\n \"breaking\": 17861,\n \"lethality\": 17862,\n \"creb\": 17863,\n \"##omeres\": 17864,\n \"##peridone\": 17865,\n \"shrinkage\": 17866,\n \"##elic\": 17867,\n \"mmc\": 17868,\n \"microf\": 17869,\n \"##plas\": 17870,\n \"puer\": 17871,\n \"anatom\": 17872,\n \"lipophilic\": 17873,\n \"hemostasis\": 17874,\n \"4th\": 17875,\n \"nonun\": 17876,\n \"digoxin\": 17877,\n \"##thermal\": 17878,\n \"tether\": 17879,\n \"##eastern\": 17880,\n \"rhabdomy\": 17881,\n \"siv\": 17882,\n \"eluting\": 17883,\n \"##osomiasis\": 17884,\n \"dtpa\": 17885,\n \"seawater\": 17886,\n \"##bur\": 17887,\n \"nigeria\": 17888,\n \"mediation\": 17889,\n \"##ogluc\": 17890,\n \"fluc\": 17891,\n \"introduces\": 17892,\n \"immortal\": 17893,\n \"##f4\": 17894,\n \"neurocognitive\": 17895,\n 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\"denitr\": 17939,\n \"cd13\": 17940,\n \"lati\": 17941,\n \"tetanus\": 17942,\n \"bandwidth\": 17943,\n \"prodrug\": 17944,\n \"organis\": 17945,\n \"educators\": 17946,\n \"situated\": 17947,\n \"melanomas\": 17948,\n \"genis\": 17949,\n \"symmetrical\": 17950,\n \"##onates\": 17951,\n \"preparing\": 17952,\n \"fna\": 17953,\n \"pulsatile\": 17954,\n \"bifurcation\": 17955,\n \"ranked\": 17956,\n \"##legia\": 17957,\n \"objectively\": 17958,\n \"cubic\": 17959,\n \"oxygenase\": 17960,\n \"refinement\": 17961,\n \"##akary\": 17962,\n \"cream\": 17963,\n \"degrade\": 17964,\n \"territory\": 17965,\n \"##ingly\": 17966,\n \"execution\": 17967,\n \"syncope\": 17968,\n \"zoon\": 17969,\n \"hairpin\": 17970,\n \"intima\": 17971,\n \"g6\": 17972,\n \"##ispens\": 17973,\n \"hyaluron\": 17974,\n \"historically\": 17975,\n \"1978\": 17976,\n \"diastere\": 17977,\n \"adequacy\": 17978,\n \"numerically\": 17979,\n \"soleus\": 17980,\n \"anxi\": 17981,\n \"##mitters\": 17982,\n 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18070,\n \"cmr\": 18071,\n \"sett\": 18072,\n \"bean\": 18073,\n \"evac\": 18074,\n \"additives\": 18075,\n \"reviewing\": 18076,\n \"outdoor\": 18077,\n \"##methyl\": 18078,\n \"reflexes\": 18079,\n \"aminoglyc\": 18080,\n \"quadru\": 18081,\n \"mycoplasma\": 18082,\n \"cloud\": 18083,\n \"connecting\": 18084,\n \"choroid\": 18085,\n \"paracrine\": 18086,\n \"##onation\": 18087,\n \"canals\": 18088,\n \"scap\": 18089,\n \"pedig\": 18090,\n \"answers\": 18091,\n \"iatrogenic\": 18092,\n \"##ocent\": 18093,\n \"michael\": 18094,\n \"substanti\": 18095,\n \"shallow\": 18096,\n \"engraft\": 18097,\n \"probabil\": 18098,\n \"autoreg\": 18099,\n \"ferm\": 18100,\n \"shaping\": 18101,\n \"fis\": 18102,\n \"##idinium\": 18103,\n \"imrt\": 18104,\n \"participates\": 18105,\n \"hundreds\": 18106,\n \"adri\": 18107,\n \"sga\": 18108,\n \"infertile\": 18109,\n \"infantile\": 18110,\n \"cycloh\": 18111,\n \"spheres\": 18112,\n \"concan\": 18113,\n \"monoxide\": 18114,\n \"5th\": 18115,\n \"herd\": 18116,\n \"endonuclease\": 18117,\n \"doi\": 18118,\n \"##lysis\": 18119,\n \"amenable\": 18120,\n \"valpro\": 18121,\n \"amphib\": 18122,\n \"0005\": 18123,\n \"glomeruli\": 18124,\n \"264\": 18125,\n \"mimicked\": 18126,\n \"symp\": 18127,\n \"synapse\": 18128,\n \"pest\": 18129,\n \"##aturity\": 18130,\n \"multist\": 18131,\n \"##apoptotic\": 18132,\n \"nmd\": 18133,\n \"trough\": 18134,\n \"cftr\": 18135,\n \"##olab\": 18136,\n \"##allic\": 18137,\n \"reviewers\": 18138,\n \"probabilistic\": 18139,\n \"expectation\": 18140,\n \"visited\": 18141,\n \"uncertainties\": 18142,\n \"heterozygosity\": 18143,\n \"muller\": 18144,\n \"##asin\": 18145,\n \"anthropogenic\": 18146,\n \"e3\": 18147,\n \"##phi\": 18148,\n \"plei\": 18149,\n \"##ropin\": 18150,\n \"##kb\": 18151,\n \"sp1\": 18152,\n \"completing\": 18153,\n \"dsc\": 18154,\n \"elementary\": 18155,\n \"hydrolyzed\": 18156,\n \"dephosph\": 18157,\n \"counterpart\": 18158,\n \"mortem\": 18159,\n \"ppd\": 18160,\n \"167\": 18161,\n 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18206,\n \"failing\": 18207,\n \"committed\": 18208,\n \"exciting\": 18209,\n \"lpa\": 18210,\n \"oleic\": 18211,\n \"mediastin\": 18212,\n \"workforce\": 18213,\n \"dwi\": 18214,\n \"submucosal\": 18215,\n \"##yron\": 18216,\n \"##rals\": 18217,\n \"axes\": 18218,\n \"secure\": 18219,\n \"hed\": 18220,\n \"##alis\": 18221,\n \"##onegative\": 18222,\n \"concre\": 18223,\n \"##atergic\": 18224,\n \"seeded\": 18225,\n \"gallst\": 18226,\n \"##anat\": 18227,\n \"##yseal\": 18228,\n \"igg4\": 18229,\n \"phylogeny\": 18230,\n \"mounted\": 18231,\n \"obstacles\": 18232,\n \"tendons\": 18233,\n \"maintains\": 18234,\n \"rigidity\": 18235,\n \"simvastatin\": 18236,\n \"##azosin\": 18237,\n \"said\": 18238,\n \"carbap\": 18239,\n \"dividing\": 18240,\n \"descriptors\": 18241,\n \"##book\": 18242,\n \"suppl\": 18243,\n \"stacking\": 18244,\n \"g3\": 18245,\n \"witness\": 18246,\n \"geometries\": 18247,\n \"##187\": 18248,\n \"##opreval\": 18249,\n \"sponge\": 18250,\n \"##86\": 18251,\n \"pleth\": 18252,\n \"##ja\": 18253,\n \"marginally\": 18254,\n \"asparag\": 18255,\n \"justified\": 18256,\n \"diphosphate\": 18257,\n \"synchronized\": 18258,\n \"##anide\": 18259,\n \"lond\": 18260,\n \"ameliorate\": 18261,\n \"eclam\": 18262,\n \"syngene\": 18263,\n \"smoked\": 18264,\n \"##ishment\": 18265,\n \"mts\": 18266,\n \"trophoblast\": 18267,\n \"##urethral\": 18268,\n \"monoamine\": 18269,\n \"conductive\": 18270,\n \"seropreval\": 18271,\n \"abnormally\": 18272,\n \"mobil\": 18273,\n \"dish\": 18274,\n \"##ximide\": 18275,\n \"methylp\": 18276,\n \"ch2\": 18277,\n \"hook\": 18278,\n \"metropolitan\": 18279,\n \"cyclodextrin\": 18280,\n \"ecmo\": 18281,\n \"song\": 18282,\n \"reprogramm\": 18283,\n \"##oretin\": 18284,\n \"worst\": 18285,\n \"genistein\": 18286,\n \"wards\": 18287,\n \"kar\": 18288,\n \"handle\": 18289,\n \"hypoplasia\": 18290,\n \"acros\": 18291,\n \"##icides\": 18292,\n \"marketing\": 18293,\n \"granuloma\": 18294,\n \"mycobacterial\": 18295,\n 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\"6j\": 18342,\n \"pharmaceuticals\": 18343,\n \"scavenger\": 18344,\n \"megakary\": 18345,\n \"thematic\": 18346,\n \"bib\": 18347,\n \"disag\": 18348,\n \"chlorine\": 18349,\n \"acinar\": 18350,\n \"fove\": 18351,\n \"sinusoidal\": 18352,\n \"syngeneic\": 18353,\n \"##orem\": 18354,\n \"##p4\": 18355,\n \"gerd\": 18356,\n \"neurotoxic\": 18357,\n \"readings\": 18358,\n \"##entistry\": 18359,\n \"##ximal\": 18360,\n \"swabs\": 18361,\n \"phenotyp\": 18362,\n \"172\": 18363,\n \"##x10\": 18364,\n \"h⋯o\": 18365,\n \"eradic\": 18366,\n \"169\": 18367,\n \"##acea\": 18368,\n \"feat\": 18369,\n \"premenopausal\": 18370,\n \"cd16\": 18371,\n \"violent\": 18372,\n \"disabled\": 18373,\n \"abstracts\": 18374,\n \"introns\": 18375,\n \"topoisomerase\": 18376,\n \"survivin\": 18377,\n \"unrespons\": 18378,\n \"acetyltransferase\": 18379,\n \"chaperone\": 18380,\n \"carboplatin\": 18381,\n \"forests\": 18382,\n \"minerals\": 18383,\n \"correspondence\": 18384,\n \"retroviral\": 18385,\n \"dispersive\": 18386,\n \"osteomyelitis\": 18387,\n \"##oly\": 18388,\n \"hhv\": 18389,\n \"glycosyl\": 18390,\n \"unpred\": 18391,\n \"rhamn\": 18392,\n \"empirically\": 18393,\n \"subtraction\": 18394,\n \"hong\": 18395,\n \"nyst\": 18396,\n \"bioch\": 18397,\n \"stricture\": 18398,\n \"adrenoceptors\": 18399,\n \"accred\": 18400,\n \"##esp\": 18401,\n \"synergistically\": 18402,\n \"larynx\": 18403,\n \"disposal\": 18404,\n \"thc\": 18405,\n \"vacuoles\": 18406,\n \"vasodilator\": 18407,\n \"mildly\": 18408,\n \"nest\": 18409,\n \"quadrup\": 18410,\n \"catalyze\": 18411,\n \"addic\": 18412,\n \"ei\": 18413,\n \"glutamatergic\": 18414,\n \"proteoglycan\": 18415,\n \"intimal\": 18416,\n \"demanding\": 18417,\n \"##imilar\": 18418,\n \"perfluor\": 18419,\n \"myeloperoxidase\": 18420,\n \"intravitreal\": 18421,\n \"eclampsia\": 18422,\n \"bead\": 18423,\n \"5d\": 18424,\n \"concanavalin\": 18425,\n \"retrovirus\": 18426,\n \"##ospecific\": 18427,\n \"##oxylin\": 18428,\n \"viet\": 18429,\n \"deacet\": 18430,\n \"trachomatis\": 18431,\n \"specially\": 18432,\n \"dangerous\": 18433,\n \"imatinib\": 18434,\n \"mth\": 18435,\n \"acp\": 18436,\n \"##arians\": 18437,\n \"physics\": 18438,\n \"icr\": 18439,\n \"cyclosporin\": 18440,\n \"##terolateral\": 18441,\n \"##inus\": 18442,\n \"lym\": 18443,\n \"##burg\": 18444,\n \"androgens\": 18445,\n \"dithi\": 18446,\n \"neurotransmitters\": 18447,\n \"212\": 18448,\n \"resembled\": 18449,\n \"monoc\": 18450,\n \"ortholog\": 18451,\n \"##sr\": 18452,\n \"gpa\": 18453,\n \"manually\": 18454,\n \"infect\": 18455,\n \"adds\": 18456,\n \"dentistry\": 18457,\n \"oryz\": 18458,\n \"##uitr\": 18459,\n \"perivascular\": 18460,\n \"reuptake\": 18461,\n \"lem\": 18462,\n \"##tryptamine\": 18463,\n \"covariance\": 18464,\n \"xanthine\": 18465,\n \"##otonic\": 18466,\n \"##inia\": 18467,\n \"ken\": 18468,\n \"adver\": 18469,\n \"##amers\": 18470,\n \"butyrate\": 18471,\n \"steroidal\": 18472,\n \"nanocrystals\": 18473,\n \"##onasal\": 18474,\n \"photoelectron\": 18475,\n \"rectus\": 18476,\n \"corrosion\": 18477,\n \"cardiology\": 18478,\n \"rhythmic\": 18479,\n \"methacrylate\": 18480,\n \"##l2\": 18481,\n \"##ocene\": 18482,\n \"epicardial\": 18483,\n \"glycans\": 18484,\n \"##ovalent\": 18485,\n \"chemopre\": 18486,\n \"##cys\": 18487,\n \"limbic\": 18488,\n \"decided\": 18489,\n \"consul\": 18490,\n \"peptic\": 18491,\n \"##anthine\": 18492,\n \"instillation\": 18493,\n \"##oflav\": 18494,\n \"nis\": 18495,\n \"suspicious\": 18496,\n \"##uctal\": 18497,\n \"176\": 18498,\n \"brdu\": 18499,\n \"elucidating\": 18500,\n \"cavernous\": 18501,\n \"constraint\": 18502,\n \"civil\": 18503,\n \"161\": 18504,\n \"nephrotic\": 18505,\n \"wearing\": 18506,\n \"brack\": 18507,\n \"##thesis\": 18508,\n \"intercourse\": 18509,\n \"orthotopic\": 18510,\n \"blo\": 18511,\n \"fragile\": 18512,\n \"niger\": 18513,\n \"pgf2\": 18514,\n \"quiescent\": 18515,\n \"msp\": 18516,\n \"lpl\": 18517,\n \"contradict\": 18518,\n \"perceive\": 18519,\n \"lasers\": 18520,\n \"funded\": 18521,\n \"lactating\": 18522,\n \"appraisal\": 18523,\n \"##athic\": 18524,\n \"supervised\": 18525,\n \"cryo\": 18526,\n \"uti\": 18527,\n \"pertaining\": 18528,\n \"pcd\": 18529,\n \"precipitated\": 18530,\n \"dab\": 18531,\n \"birthweight\": 18532,\n \"evaluable\": 18533,\n \"counted\": 18534,\n \"##anthin\": 18535,\n \"copolymers\": 18536,\n \"pph\": 18537,\n \"preincubation\": 18538,\n \"scarring\": 18539,\n \"referrals\": 18540,\n \"registries\": 18541,\n \"##agger\": 18542,\n \"integrins\": 18543,\n \"retraction\": 18544,\n \"alpha2\": 18545,\n \"bedside\": 18546,\n \"motile\": 18547,\n \"breakthrough\": 18548,\n \"##operiod\": 18549,\n \"##ymic\": 18550,\n \"ionophore\": 18551,\n \"##kd\": 18552,\n \"##ocalcin\": 18553,\n \"chemoat\": 18554,\n \"glia\": 18555,\n \"polycyclic\": 18556,\n \"yeasts\": 18557,\n \"imagery\": 18558,\n \"pean\": 18559,\n \"dissatis\": 18560,\n \"homeostatic\": 18561,\n \"##uses\": 18562,\n 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18608,\n \"dereg\": 18609,\n \"hardness\": 18610,\n \"biodegradation\": 18611,\n \"##iteal\": 18612,\n \"preexisting\": 18613,\n \"bon\": 18614,\n \"urothelial\": 18615,\n \"cancerous\": 18616,\n \"guanosine\": 18617,\n \"jejunal\": 18618,\n \"heparan\": 18619,\n \"albino\": 18620,\n \"##cus\": 18621,\n \"186\": 18622,\n \"##atars\": 18623,\n \"##ava\": 18624,\n \"##etron\": 18625,\n \"adl\": 18626,\n \"183\": 18627,\n \"##ifera\": 18628,\n \"hsp90\": 18629,\n \"contributor\": 18630,\n \"256\": 18631,\n \"morphogenetic\": 18632,\n \"##orectal\": 18633,\n \"chondroitin\": 18634,\n \"sv40\": 18635,\n \"##odys\": 18636,\n \"cddp\": 18637,\n \"cytosine\": 18638,\n \"##urred\": 18639,\n \"##thra\": 18640,\n \"arrangements\": 18641,\n \"nematodes\": 18642,\n \"##endoth\": 18643,\n \"artifact\": 18644,\n \"flour\": 18645,\n \"asph\": 18646,\n \"brass\": 18647,\n \"practically\": 18648,\n \"##lofenac\": 18649,\n \"electrocardiographic\": 18650,\n \"resonant\": 18651,\n \"uf\": 18652,\n 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\"gastroc\": 18697,\n \"autoradiography\": 18698,\n \"##t2\": 18699,\n \"macrol\": 18700,\n \"##otin\": 18701,\n \"dysk\": 18702,\n \"seroprevalence\": 18703,\n \"##ithiasis\": 18704,\n \"##ancing\": 18705,\n \"suction\": 18706,\n \"atrophic\": 18707,\n \"propionate\": 18708,\n \"021\": 18709,\n \"alex\": 18710,\n \"sequestration\": 18711,\n \"moll\": 18712,\n \"vb\": 18713,\n \"hyperalgesia\": 18714,\n \"neurosurgical\": 18715,\n \"subfamily\": 18716,\n \"prism\": 18717,\n \"unrecogn\": 18718,\n \"interior\": 18719,\n \"secondly\": 18720,\n \"fitc\": 18721,\n \"alarm\": 18722,\n \"181\": 18723,\n \"023\": 18724,\n \"acanth\": 18725,\n \"carn\": 18726,\n \"pyel\": 18727,\n \"gpcr\": 18728,\n \"anchor\": 18729,\n \"##otracheal\": 18730,\n \"insertions\": 18731,\n \"engraftment\": 18732,\n \"examin\": 18733,\n \"infiltrates\": 18734,\n \"reconstruct\": 18735,\n \"mucous\": 18736,\n \"autocrine\": 18737,\n \"lncrnas\": 18738,\n \"burd\": 18739,\n \"bovis\": 18740,\n \"insult\": 18741,\n 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18788,\n \"unamb\": 18789,\n \"tcdd\": 18790,\n \"pixel\": 18791,\n \"##articular\": 18792,\n \"mole\": 18793,\n \"dsa\": 18794,\n \"interobserver\": 18795,\n \"inoculum\": 18796,\n \"fmol\": 18797,\n \"tlr2\": 18798,\n \"oestradiol\": 18799,\n \"##89\": 18800,\n \"xr\": 18801,\n \"##ytoin\": 18802,\n \"p2x\": 18803,\n \"inconclusive\": 18804,\n \"##apsed\": 18805,\n \"persists\": 18806,\n \"##romatic\": 18807,\n \"overlooked\": 18808,\n \"4d\": 18809,\n \"##abdominal\": 18810,\n \"traps\": 18811,\n \"mm3\": 18812,\n \"formulas\": 18813,\n \"204\": 18814,\n \"centrally\": 18815,\n \"depths\": 18816,\n \"##idation\": 18817,\n \"pla2\": 18818,\n \"israel\": 18819,\n \"##oplankton\": 18820,\n \"cortices\": 18821,\n \"lor\": 18822,\n \"luteinizing\": 18823,\n \"expense\": 18824,\n \"##itidis\": 18825,\n \"flavonoid\": 18826,\n \"complexation\": 18827,\n \"unpredict\": 18828,\n \"##thoracic\": 18829,\n \"lipoxygenase\": 18830,\n \"##brand\": 18831,\n \"bursts\": 18832,\n \"tmj\": 18833,\n 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18924,\n \"imprinted\": 18925,\n \"##adel\": 18926,\n \"serogroup\": 18927,\n \"##brt\": 18928,\n \"##ticism\": 18929,\n \"succe\": 18930,\n \"pz\": 18931,\n \"glucosidase\": 18932,\n \"ultrafil\": 18933,\n \"attract\": 18934,\n \"unt\": 18935,\n \"dysfunctional\": 18936,\n \"rumen\": 18937,\n \"##rosine\": 18938,\n \"urtic\": 18939,\n \"osteoporotic\": 18940,\n \"inequalities\": 18941,\n \"##rein\": 18942,\n \"171\": 18943,\n \"hyperv\": 18944,\n \"skew\": 18945,\n \"gpx\": 18946,\n \"interacted\": 18947,\n \"regressions\": 18948,\n \"cbct\": 18949,\n \"enterococcus\": 18950,\n \"173\": 18951,\n \"passing\": 18952,\n \"transection\": 18953,\n \"nowad\": 18954,\n \"nonster\": 18955,\n \"halogen\": 18956,\n \"etching\": 18957,\n \"diversification\": 18958,\n \"criminal\": 18959,\n \"mite\": 18960,\n \"##asant\": 18961,\n \"indol\": 18962,\n \"visco\": 18963,\n \"nowadays\": 18964,\n \"##epines\": 18965,\n \"blunted\": 18966,\n \"##uitry\": 18967,\n \"migrating\": 18968,\n \"unem\": 18969,\n \"gastroesophageal\": 18970,\n \"escc\": 18971,\n \"liposomal\": 18972,\n \"##ifiers\": 18973,\n \"teacher\": 18974,\n \"vertebrae\": 18975,\n \"##mr\": 18976,\n \"##acillin\": 18977,\n \"bold\": 18978,\n \"cannabinoid\": 18979,\n \"advertis\": 18980,\n \"##icit\": 18981,\n \"accidental\": 18982,\n \"##entin\": 18983,\n \"morbidities\": 18984,\n \"amplic\": 18985,\n \"incidents\": 18986,\n \"exertion\": 18987,\n \"educated\": 18988,\n \"##enzymes\": 18989,\n \"##field\": 18990,\n \"efferent\": 18991,\n \"denture\": 18992,\n \"##prof\": 18993,\n \"dre\": 18994,\n \"reasonably\": 18995,\n \"leaflet\": 18996,\n \"conflicts\": 18997,\n \"##acies\": 18998,\n \"##gamma\": 18999,\n \"proceed\": 19000,\n \"quadriceps\": 19001,\n \"pathologically\": 19002,\n \"##eve\": 19003,\n \"malays\": 19004,\n \"thermo\": 19005,\n \"##oxamine\": 19006,\n \"osteocalcin\": 19007,\n \"rotator\": 19008,\n \"competency\": 19009,\n \"##avirin\": 19010,\n \"lax\": 19011,\n \"##iensis\": 19012,\n 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19146,\n \"##argin\": 19147,\n \"##omycosis\": 19148,\n \"mans\": 19149,\n \"##cing\": 19150,\n \"amoxicillin\": 19151,\n \"gynecologic\": 19152,\n \"uncovered\": 19153,\n \"##oca\": 19154,\n \"opn\": 19155,\n \"bark\": 19156,\n \"fiss\": 19157,\n \"##omandibular\": 19158,\n \"uremic\": 19159,\n \"nuclease\": 19160,\n \"nearby\": 19161,\n \"reductive\": 19162,\n \"exploit\": 19163,\n \"sas\": 19164,\n \"wilc\": 19165,\n \"ribavirin\": 19166,\n \"##ticle\": 19167,\n \"snap\": 19168,\n \"combinator\": 19169,\n \"svr\": 19170,\n \"##gc\": 19171,\n \"staphylococcal\": 19172,\n \"heritability\": 19173,\n \"primord\": 19174,\n \"representatives\": 19175,\n \"aminobutyric\": 19176,\n \"dnase\": 19177,\n \"##alg\": 19178,\n \"stature\": 19179,\n \"compartmental\": 19180,\n \"cbd\": 19181,\n \"gill\": 19182,\n \"eosinophilia\": 19183,\n \"chemoattract\": 19184,\n \"invasiveness\": 19185,\n \"turk\": 19186,\n \"fibros\": 19187,\n \"##oencephal\": 19188,\n \"publicly\": 19189,\n \"##vd\": 19190,\n \"mong\": 19191,\n \"mra\": 19192,\n \"##holder\": 19193,\n \"apple\": 19194,\n \"trypanosoma\": 19195,\n \"##olysin\": 19196,\n \"leaching\": 19197,\n \"saudi\": 19198,\n \"overproduc\": 19199,\n \"##electronic\": 19200,\n \"cronbach\": 19201,\n \"clues\": 19202,\n \"calcified\": 19203,\n \"therapist\": 19204,\n \"got\": 19205,\n \"bout\": 19206,\n \"kall\": 19207,\n \"scars\": 19208,\n \"##ql\": 19209,\n \"condyl\": 19210,\n \"fusarium\": 19211,\n \"attainment\": 19212,\n \"cassette\": 19213,\n \"quinone\": 19214,\n \"dext\": 19215,\n \"184\": 19216,\n \"evidences\": 19217,\n \"aliph\": 19218,\n \"leydig\": 19219,\n \"saphenous\": 19220,\n \"vice\": 19221,\n \"msi\": 19222,\n \"absorpti\": 19223,\n \"187\": 19224,\n \"multicent\": 19225,\n \"decompens\": 19226,\n \"pepsin\": 19227,\n \"kong\": 19228,\n \"condensed\": 19229,\n \"mesoporous\": 19230,\n \"##odopa\": 19231,\n \"kits\": 19232,\n \"markov\": 19233,\n \"reversibly\": 19234,\n \"##afenib\": 19235,\n \"leptosp\": 19236,\n \"##alline\": 19237,\n \"mush\": 19238,\n \"agglutination\": 19239,\n \"isopren\": 19240,\n \"##othing\": 19241,\n \"phenobarbital\": 19242,\n \"homeless\": 19243,\n \"nts\": 19244,\n \"##fly\": 19245,\n \"bubble\": 19246,\n \"interpretations\": 19247,\n \"##oinositi\": 19248,\n \"##arity\": 19249,\n \"observing\": 19250,\n \"lymphadenopathy\": 19251,\n \"##awa\": 19252,\n \"##etization\": 19253,\n \"laun\": 19254,\n \"nag\": 19255,\n \"societal\": 19256,\n \"separating\": 19257,\n \"detectors\": 19258,\n \"##astig\": 19259,\n \"mountain\": 19260,\n \"braf\": 19261,\n \"libitum\": 19262,\n \"cd28\": 19263,\n \"aeg\": 19264,\n \"risky\": 19265,\n \"constantly\": 19266,\n \"letters\": 19267,\n \"pumps\": 19268,\n \"##plicates\": 19269,\n \"recap\": 19270,\n \"interrel\": 19271,\n \"transaminase\": 19272,\n \"##opathological\": 19273,\n \"##antit\": 19274,\n \"assemble\": 19275,\n \"kp\": 19276,\n \"reabsorption\": 19277,\n \"affiliated\": 19278,\n \"mycobacteria\": 19279,\n \"histomorph\": 19280,\n \"operators\": 19281,\n \"##axin\": 19282,\n \"zoster\": 19283,\n \"cfs\": 19284,\n \"kel\": 19285,\n \"##epid\": 19286,\n \"cpa\": 19287,\n \"kat\": 19288,\n \"##acrimal\": 19289,\n \"perovsk\": 19290,\n \"classifier\": 19291,\n \"proliferator\": 19292,\n \"probnp\": 19293,\n \"cmc\": 19294,\n \"culturing\": 19295,\n \"economically\": 19296,\n \"sarcoplas\": 19297,\n \"arres\": 19298,\n \"counties\": 19299,\n \"disinfection\": 19300,\n \"serr\": 19301,\n \"expenditures\": 19302,\n \"pcc\": 19303,\n \"5000\": 19304,\n \"infliximab\": 19305,\n \"##oco\": 19306,\n \"lobectomy\": 19307,\n \"binocular\": 19308,\n \"##formation\": 19309,\n \"hcmv\": 19310,\n \"beverages\": 19311,\n \"##ulline\": 19312,\n \"associates\": 19313,\n \"##otrypsin\": 19314,\n \"haematological\": 19315,\n \"deoxyrib\": 19316,\n \"purely\": 19317,\n \"cheese\": 19318,\n \"ban\": 19319,\n \"elastin\": 19320,\n \"##ogenin\": 19321,\n \"backward\": 19322,\n \"egcg\": 19323,\n \"psychiatr\": 19324,\n \"hsd\": 19325,\n \"thermally\": 19326,\n \"disrupting\": 19327,\n \"##alo\": 19328,\n \"dural\": 19329,\n \"nervosa\": 19330,\n \"fermented\": 19331,\n \"bacilli\": 19332,\n \"calpain\": 19333,\n \"anesthetics\": 19334,\n \"sers\": 19335,\n \"##occl\": 19336,\n \"etoh\": 19337,\n \"208\": 19338,\n \"eccentric\": 19339,\n \"ornithine\": 19340,\n \"hyperg\": 19341,\n \"psychomotor\": 19342,\n \"ribosomes\": 19343,\n \"sacral\": 19344,\n \"035\": 19345,\n \"latit\": 19346,\n \"dysfunctions\": 19347,\n \"sectors\": 19348,\n \"##oped\": 19349,\n \"pericard\": 19350,\n \"entail\": 19351,\n \"edition\": 19352,\n \"tetrah\": 19353,\n \"##iparous\": 19354,\n \"constitution\": 19355,\n \"anticoagulants\": 19356,\n \"216\": 19357,\n \"##oresp\": 19358,\n \"##itives\": 19359,\n \"hindlim\": 19360,\n \"ruled\": 19361,\n \"discussing\": 19362,\n \"dorm\": 19363,\n \"holistic\": 19364,\n \"gastrocnem\": 19365,\n \"aliphatic\": 19366,\n \"emitted\": 19367,\n \"recreational\": 19368,\n \"compute\": 19369,\n \"antih\": 19370,\n \"wille\": 19371,\n \"pls\": 19372,\n \"##aneurys\": 19373,\n \"endocrin\": 19374,\n \"##ofemoral\": 19375,\n \"manuscript\": 19376,\n \"##ellae\": 19377,\n \"sharply\": 19378,\n \"chemiluminescence\": 19379,\n \"weal\": 19380,\n \"##idomide\": 19381,\n \"voiding\": 19382,\n \"ultrasonographic\": 19383,\n \"obsessive\": 19384,\n \"##appab\": 19385,\n \"##ronchial\": 19386,\n \"##eptin\": 19387,\n \"hmgb1\": 19388,\n \"lakes\": 19389,\n \"shaft\": 19390,\n \"ketone\": 19391,\n \"ambiguous\": 19392,\n \"tas\": 19393,\n \"genesis\": 19394,\n \"##fin\": 19395,\n \"clay\": 19396,\n \"wel\": 19397,\n \"lesioned\": 19398,\n \"##mn\": 19399,\n \"vincr\": 19400,\n \"consortium\": 19401,\n \"simplicity\": 19402,\n \"coral\": 19403,\n \"absorptiometry\": 19404,\n \"bacteriophage\": 19405,\n \"##osterol\": 19406,\n \"mesothelioma\": 19407,\n \"damages\": 19408,\n \"voltammetry\": 19409,\n \"gout\": 19410,\n \"##yelin\": 19411,\n \"exagger\": 19412,\n \"p300\": 19413,\n \"3000\": 19414,\n \"seriously\": 19415,\n \"ankyl\": 19416,\n \"gynaec\": 19417,\n \"chop\": 19418,\n \"followup\": 19419,\n \"carbamazepine\": 19420,\n \"urgently\": 19421,\n \"certified\": 19422,\n \"##imethyl\": 19423,\n \"lingual\": 19424,\n \"carboxylate\": 19425,\n \"masking\": 19426,\n \"thior\": 19427,\n \"saharan\": 19428,\n \"330\": 19429,\n \"recognizes\": 19430,\n \"kap\": 19431,\n \"##anda\": 19432,\n \"contaminant\": 19433,\n \"##agens\": 19434,\n \"##rows\": 19435,\n \"dnas\": 19436,\n \"outlet\": 19437,\n \"##oreflex\": 19438,\n \"impulsivity\": 19439,\n \"withdrawn\": 19440,\n \"b16\": 19441,\n \"colloid\": 19442,\n \"inev\": 19443,\n \"producers\": 19444,\n \"##ubstituted\": 19445,\n \"tca\": 19446,\n \"spo\": 19447,\n \"##anum\": 19448,\n \"worsened\": 19449,\n \"##yline\": 19450,\n \"bloodstream\": 19451,\n \"##tigo\": 19452,\n \"##adap\": 19453,\n \"thoughts\": 19454,\n \"immunophen\": 19455,\n \"punct\": 19456,\n \"annular\": 19457,\n 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19501,\n \"levodopa\": 19502,\n \"endotracheal\": 19503,\n \"electrolytes\": 19504,\n \"modifiable\": 19505,\n \"maximally\": 19506,\n \"galectin\": 19507,\n \"distinctly\": 19508,\n \"hexagonal\": 19509,\n \"0002\": 19510,\n \"throat\": 19511,\n \"cid\": 19512,\n \"lanth\": 19513,\n \"multicentre\": 19514,\n \"zol\": 19515,\n \"outputs\": 19516,\n \"ocul\": 19517,\n \"proxy\": 19518,\n \"lub\": 19519,\n \"##rb\": 19520,\n \"extravas\": 19521,\n \"pediatr\": 19522,\n \"parasympathetic\": 19523,\n \"##acetamol\": 19524,\n \"vv\": 19525,\n \"ortho\": 19526,\n \"intu\": 19527,\n \"##oviral\": 19528,\n \"partnership\": 19529,\n \"contraceptives\": 19530,\n \"xylose\": 19531,\n \"pect\": 19532,\n \"stains\": 19533,\n \"fibrinolytic\": 19534,\n \"hither\": 19535,\n \"gastrocnemius\": 19536,\n \"rins\": 19537,\n \"willebrand\": 19538,\n \"vincristine\": 19539,\n \"encl\": 19540,\n \"238\": 19541,\n \"categorical\": 19542,\n \"wise\": 19543,\n \"179\": 19544,\n \"214\": 19545,\n \"loh\": 19546,\n \"gil\": 19547,\n \"styles\": 19548,\n \"resolving\": 19549,\n \"semiquantit\": 19550,\n \"sustainability\": 19551,\n \"##adish\": 19552,\n \"franc\": 19553,\n \"governed\": 19554,\n \"shortage\": 19555,\n \"eut\": 19556,\n \"##wi\": 19557,\n \"##adesh\": 19558,\n \"##analysis\": 19559,\n \"hitherto\": 19560,\n \"circuitry\": 19561,\n \"versa\": 19562,\n \"##ults\": 19563,\n \"##oxidation\": 19564,\n \"##asion\": 19565,\n \"genders\": 19566,\n \"unemploy\": 19567,\n \"pollutant\": 19568,\n \"isotropic\": 19569,\n \"209\": 19570,\n \"necrops\": 19571,\n \"cryptospor\": 19572,\n \"leis\": 19573,\n \"loosening\": 19574,\n \"fellows\": 19575,\n \"##ocyanine\": 19576,\n \"bidirectional\": 19577,\n \"associative\": 19578,\n \"arthrop\": 19579,\n \"##artum\": 19580,\n \"unmod\": 19581,\n \"stations\": 19582,\n \"##ald\": 19583,\n \"antimicrobials\": 19584,\n \"hexane\": 19585,\n \"seronegative\": 19586,\n \"practicing\": 19587,\n \"##uretics\": 19588,\n \"meiosis\": 19589,\n \"##osting\": 19590,\n \"adopting\": 19591,\n \"texas\": 19592,\n \"nephrotoxicity\": 19593,\n \"pine\": 19594,\n \"restorative\": 19595,\n \"pale\": 19596,\n \"##tz\": 19597,\n \"193\": 19598,\n \"tetrachlor\": 19599,\n \"redundant\": 19600,\n \"sustaining\": 19601,\n \"furos\": 19602,\n \"coincided\": 19603,\n \"landmarks\": 19604,\n \"dece\": 19605,\n \"##tistic\": 19606,\n \"tryptic\": 19607,\n \"##ussion\": 19608,\n \"##oconjug\": 19609,\n \"vals\": 19610,\n \"biotrans\": 19611,\n \"ontology\": 19612,\n \"##agglutination\": 19613,\n \"##iodarone\": 19614,\n \"generator\": 19615,\n \"nash\": 19616,\n \"vitrectomy\": 19617,\n \"neuroscience\": 19618,\n \"hypon\": 19619,\n \"handic\": 19620,\n \"oncological\": 19621,\n \"glycolytic\": 19622,\n \"hypertonic\": 19623,\n \"dimerization\": 19624,\n \"identifiable\": 19625,\n \"exceeds\": 19626,\n \"physiotherapy\": 19627,\n \"regenerated\": 19628,\n \"##onide\": 19629,\n \"manufacturers\": 19630,\n \"devoted\": 19631,\n \"ligase\": 19632,\n \"virological\": 19633,\n \"rumin\": 19634,\n \"artem\": 19635,\n \"photochemical\": 19636,\n \"##whel\": 19637,\n \"hps\": 19638,\n \"attraction\": 19639,\n \"##west\": 19640,\n \"##actam\": 19641,\n \"psp\": 19642,\n \"##trained\": 19643,\n \"pheochrom\": 19644,\n \"relatedness\": 19645,\n \"feline\": 19646,\n \"grp\": 19647,\n \"benzodiazepines\": 19648,\n \"complained\": 19649,\n \"schistosomiasis\": 19650,\n \"sio2\": 19651,\n \"##osyltransferase\": 19652,\n \"hnscc\": 19653,\n \"russ\": 19654,\n \"carotenoids\": 19655,\n \"lymphadenectomy\": 19656,\n \"ultraf\": 19657,\n \"generalization\": 19658,\n \"##aro\": 19659,\n \"click\": 19660,\n \"splenocytes\": 19661,\n \"##yal\": 19662,\n \"spasm\": 19663,\n \"chelating\": 19664,\n \"nearest\": 19665,\n \"##ariae\": 19666,\n \"##side\": 19667,\n \"rainfall\": 19668,\n \"sst\": 19669,\n \"##ateness\": 19670,\n \"clp\": 19671,\n \"nong\": 19672,\n \"hypercap\": 19673,\n \"anaplastic\": 19674,\n \"##f3\": 19675,\n \"furosemide\": 19676,\n \"microch\": 19677,\n \"tlc\": 19678,\n \"diamond\": 19679,\n \"##obenzene\": 19680,\n \"chose\": 19681,\n \"intervertebral\": 19682,\n \"airflow\": 19683,\n \"proliferate\": 19684,\n \"pupil\": 19685,\n \"##une\": 19686,\n \"eventual\": 19687,\n \"phonological\": 19688,\n \"##osperm\": 19689,\n \"fle\": 19690,\n \"synucle\": 19691,\n \"permeabil\": 19692,\n \"favorably\": 19693,\n \"predispose\": 19694,\n \"discordant\": 19695,\n \"tma\": 19696,\n \"caucasians\": 19697,\n \"noradrenergic\": 19698,\n \"reacts\": 19699,\n \"drinks\": 19700,\n \"##trac\": 19701,\n \"herniation\": 19702,\n \"official\": 19703,\n \"overwhel\": 19704,\n \"mmse\": 19705,\n \"leisure\": 19706,\n \"citiz\": 19707,\n \"##kg\": 19708,\n \"##ophor\": 19709,\n \"metastas\": 19710,\n \"horser\": 19711,\n \"##ophthalm\": 19712,\n \"pige\": 19713,\n \"referring\": 19714,\n \"prepro\": 19715,\n \"streams\": 19716,\n \"returning\": 19717,\n \"##so4\": 19718,\n \"wait\": 19719,\n \"och\": 19720,\n \"concrete\": 19721,\n \"heifers\": 19722,\n \"microfil\": 19723,\n \"diuretics\": 19724,\n \"coexisting\": 19725,\n \"##ume\": 19726,\n \"esbl\": 19727,\n \"carriage\": 19728,\n \"toxoplasma\": 19729,\n \"circumferential\": 19730,\n \"raph\": 19731,\n \"transthoracic\": 19732,\n \"##odynamically\": 19733,\n \"spines\": 19734,\n \"##bra\": 19735,\n \"homologues\": 19736,\n \"vulv\": 19737,\n \"compressed\": 19738,\n \"rag\": 19739,\n \"bifid\": 19740,\n \"overdose\": 19741,\n \"##osc\": 19742,\n \"cleared\": 19743,\n \"##aut\": 19744,\n \"heterosexual\": 19745,\n \"##otecan\": 19746,\n \"nicotinamide\": 19747,\n \"colonized\": 19748,\n \"##erh\": 19749,\n \"##ref\": 19750,\n \"cyclization\": 19751,\n \"internalized\": 19752,\n \"beck\": 19753,\n \"closest\": 19754,\n \"233\": 19755,\n \"magnetization\": 19756,\n \"broken\": 19757,\n \"implicate\": 19758,\n \"sarcoplasmic\": 19759,\n \"compete\": 19760,\n \"##pregn\": 19761,\n \"facet\": 19762,\n \"simulator\": 19763,\n \"##83\": 19764,\n \"geometrical\": 19765,\n \"p2y\": 19766,\n \"horseradish\": 19767,\n \"pmma\": 19768,\n \"batteries\": 19769,\n \"harbor\": 19770,\n \"serology\": 19771,\n \"##ophytes\": 19772,\n \"##obacterial\": 19773,\n \"huvecs\": 19774,\n \"##umann\": 19775,\n \"carcinogens\": 19776,\n \"impregn\": 19777,\n \"preferable\": 19778,\n \"complementation\": 19779,\n \"dcm\": 19780,\n \"##omon\": 19781,\n \"1990s\": 19782,\n \"aperture\": 19783,\n \"cardior\": 19784,\n \"215\": 19785,\n \"paracetamol\": 19786,\n \"empathy\": 19787,\n \"concentric\": 19788,\n \"outstanding\": 19789,\n \"itp\": 19790,\n \"transdermal\": 19791,\n \"##eri\": 19792,\n \"gard\": 19793,\n \"dosimetry\": 19794,\n \"intramedullary\": 19795,\n \"myristate\": 19796,\n \"regenerating\": 19797,\n \"207\": 19798,\n \"contrasts\": 19799,\n \"##oplasmosis\": 19800,\n \"forec\": 19801,\n \"myopathy\": 19802,\n \"refuge\": 19803,\n \"g4\": 19804,\n \"femt\": 19805,\n \"attribute\": 19806,\n \"hypotensive\": 19807,\n \"barc\": 19808,\n \"bq\": 19809,\n \"nations\": 19810,\n \"disadvantage\": 19811,\n \"annotated\": 19812,\n \"achieves\": 19813,\n \"imidazole\": 19814,\n \"navig\": 19815,\n \"gingivalis\": 19816,\n \"crosslinking\": 19817,\n \"t1d\": 19818,\n \"noncom\": 19819,\n \"diol\": 19820,\n \"##fas\": 19821,\n \"##una\": 19822,\n \"223\": 19823,\n \"urethra\": 19824,\n \"##ofer\": 19825,\n \"hindered\": 19826,\n \"htt\": 19827,\n \"resonances\": 19828,\n \"fluoroscopy\": 19829,\n \"cim\": 19830,\n \"enum\": 19831,\n \"hyaluronic\": 19832,\n \"##oxycycl\": 19833,\n \"phenytoin\": 19834,\n \"realize\": 19835,\n \"##erae\": 19836,\n \"##urized\": 19837,\n \"mptp\": 19838,\n \"sleeping\": 19839,\n \"epididymal\": 19840,\n \"persistently\": 19841,\n \"cdi\": 19842,\n \"synuclein\": 19843,\n \"##ostom\": 19844,\n \"##alcoh\": 19845,\n \"discipline\": 19846,\n \"osmolality\": 19847,\n \"linkages\": 19848,\n \"contacted\": 19849,\n \"toxicology\": 19850,\n \"derivatization\": 19851,\n \"harbour\": 19852,\n \"##ometers\": 19853,\n \"tubal\": 19854,\n \"graduates\": 19855,\n \"footpr\": 19856,\n \"john\": 19857,\n \"ost\": 19858,\n \"##lt\": 19859,\n \"##lyp\": 19860,\n \"biomolecules\": 19861,\n \"tram\": 19862,\n \"##ubertal\": 19863,\n \"toe\": 19864,\n \"opposing\": 19865,\n \"hbc\": 19866,\n \"##iferous\": 19867,\n \"intratum\": 19868,\n \"natal\": 19869,\n \"##oprol\": 19870,\n \"tnfα\": 19871,\n \"blots\": 19872,\n \"cooking\": 19873,\n \"clades\": 19874,\n \"cep\": 19875,\n \"receipt\": 19876,\n \"diversion\": 19877,\n \"##ophene\": 19878,\n \"antral\": 19879,\n \"antithrombin\": 19880,\n \"request\": 19881,\n \"reprogramming\": 19882,\n \"awa\": 19883,\n \"##ohem\": 19884,\n \"cyn\": 19885,\n \"gastroenteritis\": 19886,\n \"glycosides\": 19887,\n \"##ikrein\": 19888,\n \"##101\": 19889,\n \"1200\": 19890,\n \"performs\": 19891,\n \"increments\": 19892,\n \"bell\": 19893,\n \"17beta\": 19894,\n \"##annab\": 19895,\n \"221\": 19896,\n \"anesthesi\": 19897,\n \"scrap\": 19898,\n \"##took\": 19899,\n \"skilled\": 19900,\n \"stoichiometry\": 19901,\n \"##ubercul\": 19902,\n \"##bp1\": 19903,\n \"tunneling\": 19904,\n \"locked\": 19905,\n \"unsatisfactory\": 19906,\n \"a23\": 19907,\n \"fic\": 19908,\n \"css\": 19909,\n \"dynamically\": 19910,\n \"aberration\": 19911,\n \"viscoelastic\": 19912,\n \"308\": 19913,\n \"amplify\": 19914,\n \"isoflav\": 19915,\n \"antithrombotic\": 19916,\n \"##urus\": 19917,\n \"shelf\": 19918,\n \"bronchoscopy\": 19919,\n \"expresses\": 19920,\n \"obstetrics\": 19921,\n \"entrap\": 19922,\n \"lncrna\": 19923,\n \"adoptive\": 19924,\n \"benth\": 19925,\n \"snake\": 19926,\n \"probiotics\": 19927,\n \"agitation\": 19928,\n \"n2o\": 19929,\n \"slopes\": 19930,\n \"wires\": 19931,\n \"##adenosine\": 19932,\n \"comprehensively\": 19933,\n \"##avian\": 19934,\n \"h3k\": 19935,\n \"vire\": 19936,\n \"migrated\": 19937,\n \"myocarditis\": 19938,\n \"211\": 19939,\n \"housed\": 19940,\n \"hyperpolar\": 19941,\n \"inr\": 19942,\n \"unrecognized\": 19943,\n \"pigments\": 19944,\n \"bronchodil\": 19945,\n \"##130\": 19946,\n \"##eb\": 19947,\n \"340\": 19948,\n \"cd11b\": 19949,\n \"##oeba\": 19950,\n \"nitropr\": 19951,\n \"hydatid\": 19952,\n \"pvp\": 19953,\n \"paradoxical\": 19954,\n \"##ap1\": 19955,\n \"##othyron\": 19956,\n \"##anter\": 19957,\n \"nhe\": 19958,\n \"##etrically\": 19959,\n \"##aka\": 19960,\n \"##entation\": 19961,\n \"daughter\": 19962,\n \"550\": 19963,\n \"correcting\": 19964,\n \"##romatosis\": 19965,\n \"mars\": 19966,\n \"undertook\": 19967,\n \"mitomycin\": 19968,\n \"##pm\": 19969,\n \"synthesizing\": 19970,\n \"ces\": 19971,\n \"nitropruss\": 19972,\n \"cu2\": 19973,\n \"##encephalic\": 19974,\n \"diminish\": 19975,\n \"tables\": 19976,\n \"##d6\": 19977,\n \"varicella\": 19978,\n \"bronchitis\": 19979,\n \"##len\": 19980,\n \"lexical\": 19981,\n \"palmitate\": 19982,\n \"aborig\": 19983,\n \"durable\": 19984,\n \"##ochrome\": 19985,\n \"muss\": 19986,\n \"remediation\": 19987,\n 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\"translocations\": 20976,\n \"insec\": 20977,\n \"wavelet\": 20978,\n \"##umber\": 20979,\n \"048\": 20980,\n \"risperidone\": 20981,\n \"cardiorespiratory\": 20982,\n \"sinusitis\": 20983,\n \"pvn\": 20984,\n \"pumping\": 20985,\n \"convinc\": 20986,\n \"predation\": 20987,\n \"##ugr\": 20988,\n \"organelle\": 20989,\n \"solvation\": 20990,\n \"cystectomy\": 20991,\n \"##ipenem\": 20992,\n \"subma\": 20993,\n \"chemoattractant\": 20994,\n \"##can\": 20995,\n \"ddt\": 20996,\n \"calorie\": 20997,\n \"exhaus\": 20998,\n \"##prop\": 20999,\n \"##omorphine\": 21000,\n \"flatten\": 21001,\n \"vertigo\": 21002,\n \"congeners\": 21003,\n \"roentgen\": 21004,\n \"##osteal\": 21005,\n \"4000\": 21006,\n \"anonymous\": 21007,\n \"exploiting\": 21008,\n \"kan\": 21009,\n \"apd\": 21010,\n \"sentence\": 21011,\n \"##angitis\": 21012,\n \"##imeric\": 21013,\n \"hypoglycemic\": 21014,\n \"pbc\": 21015,\n \"ell\": 21016,\n \"transparency\": 21017,\n \"dpph\": 21018,\n \"extravasation\": 21019,\n \"terminally\": 21020,\n \"ipa\": 21021,\n \"tace\": 21022,\n \"dnp\": 21023,\n \"##etomidine\": 21024,\n \"argument\": 21025,\n \"gabaa\": 21026,\n \"loose\": 21027,\n \"unbiased\": 21028,\n \"232\": 21029,\n \"pacap\": 21030,\n \"vz\": 21031,\n \"norweg\": 21032,\n \"##atia\": 21033,\n \"tpo\": 21034,\n \"icg\": 21035,\n \"arthroscopy\": 21036,\n \"aca\": 21037,\n \"organizing\": 21038,\n \"insemination\": 21039,\n \"##imumab\": 21040,\n \"marks\": 21041,\n \"formyl\": 21042,\n \"saturable\": 21043,\n \"exchanges\": 21044,\n \"##illed\": 21045,\n \"developmentally\": 21046,\n \"hematuria\": 21047,\n \"asphyx\": 21048,\n \"responder\": 21049,\n \"xer\": 21050,\n \"implied\": 21051,\n \"##isen\": 21052,\n \"enthalpy\": 21053,\n \"neoin\": 21054,\n \"aband\": 21055,\n \"##ila\": 21056,\n \"appreciated\": 21057,\n \"tmp\": 21058,\n \"presump\": 21059,\n \"##abl\": 21060,\n \"bioge\": 21061,\n \"biotic\": 21062,\n \"hyperint\": 21063,\n \"cystein\": 21064,\n \"##eto\": 21065,\n \"fluency\": 21066,\n \"bent\": 21067,\n \"probands\": 21068,\n \"lymphoproliferative\": 21069,\n \"##renorphine\": 21070,\n \"photosensitiz\": 21071,\n \"orex\": 21072,\n \"valley\": 21073,\n \"lov\": 21074,\n \"amil\": 21075,\n \"224\": 21076,\n \"quadrupole\": 21077,\n \"##cnts\": 21078,\n \"isothi\": 21079,\n \"avenues\": 21080,\n \"##olaryng\": 21081,\n \"##omyelin\": 21082,\n \"hemic\": 21083,\n \"permissive\": 21084,\n \"connect\": 21085,\n \"intracereb\": 21086,\n \"ripening\": 21087,\n \"crust\": 21088,\n \"termini\": 21089,\n \"reside\": 21090,\n \"##repres\": 21091,\n \"membership\": 21092,\n \"hyperpolarization\": 21093,\n \"quoti\": 21094,\n \"aluminium\": 21095,\n \"##oproteins\": 21096,\n \"isozymes\": 21097,\n \"coincident\": 21098,\n \"nystagmus\": 21099,\n \"##ermined\": 21100,\n \"stimulant\": 21101,\n \"omeprazole\": 21102,\n \"fcr\": 21103,\n \"superimposed\": 21104,\n \"turkish\": 21105,\n \"sre\": 21106,\n \"rises\": 21107,\n \"##openic\": 21108,\n \"maxilla\": 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\"recurred\": 21200,\n \"compelling\": 21201,\n \"phac\": 21202,\n \"xps\": 21203,\n \"biceps\": 21204,\n \"streptococcal\": 21205,\n \"pao2\": 21206,\n \"gavage\": 21207,\n \"detrusor\": 21208,\n \"fasci\": 21209,\n \"enalapril\": 21210,\n \"regi\": 21211,\n \"nonsmokers\": 21212,\n \"intraluminal\": 21213,\n \"pmns\": 21214,\n \"##ollagen\": 21215,\n \"barrel\": 21216,\n \"elicits\": 21217,\n \"detects\": 21218,\n \"##oposterior\": 21219,\n \"mate\": 21220,\n \"1950\": 21221,\n \"pseudoaneurys\": 21222,\n \"##eor\": 21223,\n \"orph\": 21224,\n \"##kat\": 21225,\n \"fried\": 21226,\n \"weighting\": 21227,\n \"propos\": 21228,\n \"pps\": 21229,\n \"manager\": 21230,\n \"eliciting\": 21231,\n \"cannula\": 21232,\n \"##tert\": 21233,\n \"neurobiological\": 21234,\n \"enjoy\": 21235,\n \"powered\": 21236,\n \"camer\": 21237,\n \"##anar\": 21238,\n \"buds\": 21239,\n \"047\": 21240,\n \"photoreceptors\": 21241,\n \"pipeline\": 21242,\n \"##iry\": 21243,\n \"childbirth\": 21244,\n 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\"peptidase\": 21332,\n \"##opterin\": 21333,\n \"bronchiol\": 21334,\n \"porphyr\": 21335,\n \"myocl\": 21336,\n \"contracture\": 21337,\n \"lacrimal\": 21338,\n \"densit\": 21339,\n \"ccc\": 21340,\n \"##ophyte\": 21341,\n \"walled\": 21342,\n \"sentences\": 21343,\n \"dub\": 21344,\n \"##mph\": 21345,\n \"##ismus\": 21346,\n \"citric\": 21347,\n \"mx\": 21348,\n \"cinahl\": 21349,\n \"kras\": 21350,\n \"estrous\": 21351,\n \"antiinflammatory\": 21352,\n \"embedding\": 21353,\n \"##annel\": 21354,\n \"nascent\": 21355,\n \"contiguous\": 21356,\n \"huvec\": 21357,\n \"barrett\": 21358,\n \"ambig\": 21359,\n \"oxaliplatin\": 21360,\n \"##admin\": 21361,\n \"phyto\": 21362,\n \"equivalence\": 21363,\n \"virions\": 21364,\n \"039\": 21365,\n \"gwas\": 21366,\n \"≥2\": 21367,\n \"compliant\": 21368,\n \"accumulates\": 21369,\n \"##reen\": 21370,\n \"dobut\": 21371,\n \"##king\": 21372,\n \"tuberc\": 21373,\n \"##odynia\": 21374,\n \"odn\": 21375,\n \"academy\": 21376,\n \"tz\": 21377,\n \"##d4\": 21378,\n \"365\": 21379,\n \"actinomyc\": 21380,\n \"seventh\": 21381,\n \"urological\": 21382,\n \"parkinsonism\": 21383,\n \"##itical\": 21384,\n \"cylinder\": 21385,\n \"hydrated\": 21386,\n \"laev\": 21387,\n \"bleaching\": 21388,\n \"hatch\": 21389,\n \"inducers\": 21390,\n \"baumann\": 21391,\n \"##roventricular\": 21392,\n \"micellar\": 21393,\n \"##6a\": 21394,\n \"microinjection\": 21395,\n \"vascularization\": 21396,\n \"accomplish\": 21397,\n \"##osseous\": 21398,\n \"217\": 21399,\n \"monolith\": 21400,\n \"imperf\": 21401,\n \"##western\": 21402,\n \"anca\": 21403,\n \"g0\": 21404,\n \"maxillofacial\": 21405,\n \"unadjusted\": 21406,\n \"divisions\": 21407,\n \"meniscus\": 21408,\n \"unaccept\": 21409,\n \"waveforms\": 21410,\n \"niddm\": 21411,\n \"extant\": 21412,\n \"integrates\": 21413,\n \"manufacturer\": 21414,\n \"ops\": 21415,\n \"##tices\": 21416,\n \"rtms\": 21417,\n \"sab\": 21418,\n \"zip\": 21419,\n \"rls\": 21420,\n \"exo\": 21421,\n \"##entioned\": 21422,\n \"##gly\": 21423,\n \"ultrac\": 21424,\n \"pegylated\": 21425,\n \"thorax\": 21426,\n \"##rum\": 21427,\n \"formerly\": 21428,\n \"sulfhydryl\": 21429,\n \"yb\": 21430,\n \"##ozoites\": 21431,\n \"norwegian\": 21432,\n \"239\": 21433,\n \"dobutamine\": 21434,\n \"fibromy\": 21435,\n \"iugr\": 21436,\n \"kines\": 21437,\n \"hay\": 21438,\n \"stressor\": 21439,\n \"pparγ\": 21440,\n \"##urge\": 21441,\n \"opa\": 21442,\n \"semiquantitative\": 21443,\n \"##eping\": 21444,\n \"cch\": 21445,\n \"palpable\": 21446,\n \"##mb\": 21447,\n \"pfge\": 21448,\n \"universities\": 21449,\n \"intracerebroventricular\": 21450,\n \"locoreg\": 21451,\n \"infarcts\": 21452,\n \"stereotyp\": 21453,\n \"moderated\": 21454,\n \"laying\": 21455,\n \"argues\": 21456,\n \"submaximal\": 21457,\n \"tig\": 21458,\n \"sati\": 21459,\n \"apomorphine\": 21460,\n \"geor\": 21461,\n \"ppt\": 21462,\n \"gob\": 21463,\n \"fission\": 21464,\n \"tps\": 21465,\n \"homogenous\": 21466,\n \"pentobarbital\": 21467,\n \"e7\": 21468,\n \"dnmt\": 21469,\n \"genotoxicity\": 21470,\n \"accelerating\": 21471,\n \"metagen\": 21472,\n \"##elt\": 21473,\n \"reposition\": 21474,\n \"chile\": 21475,\n \"ultrafiltration\": 21476,\n \"story\": 21477,\n \"radon\": 21478,\n \"##osylated\": 21479,\n \"sphingomyelin\": 21480,\n \"aseptic\": 21481,\n \"bridged\": 21482,\n \"tracheostomy\": 21483,\n \"raphe\": 21484,\n \"5a\": 21485,\n \"submandibular\": 21486,\n \"hispan\": 21487,\n \"mcg\": 21488,\n \"popliteal\": 21489,\n \"imping\": 21490,\n \"mainst\": 21491,\n \"##isy\": 21492,\n \"equilibr\": 21493,\n \"##orespons\": 21494,\n \"pellet\": 21495,\n \"##unts\": 21496,\n \"cultivar\": 21497,\n \"unload\": 21498,\n \"##erts\": 21499,\n \"##biosis\": 21500,\n \"nil\": 21501,\n \"anchoring\": 21502,\n \"ideally\": 21503,\n \"localisation\": 21504,\n \"dichotom\": 21505,\n \"##feri\": 21506,\n \"amphiphilic\": 21507,\n \"micromolar\": 21508,\n \"advised\": 21509,\n \"thicknesses\": 21510,\n \"##encephalon\": 21511,\n \"hypogonad\": 21512,\n \"##ronic\": 21513,\n \"azath\": 21514,\n \"##olstein\": 21515,\n \"candidiasis\": 21516,\n \"fviii\": 21517,\n \"lmp\": 21518,\n \"csc\": 21519,\n \"##izability\": 21520,\n \"accommodation\": 21521,\n \"nonresp\": 21522,\n \"mace\": 21523,\n \"storm\": 21524,\n \"##opar\": 21525,\n \"##aks\": 21526,\n \"microbe\": 21527,\n \"npv\": 21528,\n \"c16\": 21529,\n \"faeces\": 21530,\n \"hydroxybut\": 21531,\n \"ptca\": 21532,\n \"hematopoiesis\": 21533,\n \"chlorhex\": 21534,\n \"##ype\": 21535,\n \"paramount\": 21536,\n \"##lorinated\": 21537,\n \"380\": 21538,\n \"deeply\": 21539,\n \"##illes\": 21540,\n \"cq\": 21541,\n \"##ighter\": 21542,\n \"s1p\": 21543,\n \"t0\": 21544,\n \"##igan\": 21545,\n \"thermost\": 21546,\n \"cnts\": 21547,\n \"##ildenafil\": 21548,\n \"##alcoholic\": 21549,\n \"thf\": 21550,\n \"opto\": 21551,\n \"mantle\": 21552,\n \"directors\": 21553,\n \"imprinting\": 21554,\n \"herbs\": 21555,\n \"275\": 21556,\n \"##uis\": 21557,\n \"responsibilities\": 21558,\n \"mn2\": 21559,\n \"topographic\": 21560,\n \"bees\": 21561,\n \"wisc\": 21562,\n \"3b\": 21563,\n \"radiolig\": 21564,\n \"nitroso\": 21565,\n \"epidermidis\": 21566,\n \"carib\": 21567,\n \"immunologically\": 21568,\n \"nnos\": 21569,\n \"##gp\": 21570,\n \"marriage\": 21571,\n \"ptb\": 21572,\n \"hypoglycaemia\": 21573,\n \"gay\": 21574,\n \"oxidored\": 21575,\n \"propria\": 21576,\n \"sink\": 21577,\n \"chromophore\": 21578,\n \"##ynyl\": 21579,\n \"incompatible\": 21580,\n \"azathiop\": 21581,\n \"solubilization\": 21582,\n \"rarity\": 21583,\n \"neuroh\": 21584,\n \"7th\": 21585,\n \"neisseria\": 21586,\n \"crime\": 21587,\n \"localised\": 21588,\n \"rebound\": 21589,\n \"fon\": 21590,\n \"irf\": 21591,\n \"mailed\": 21592,\n \"hcm\": 21593,\n \"seedling\": 21594,\n \"brit\": 21595,\n \"pml\": 21596,\n \"oscillator\": 21597,\n \"heterodimer\": 21598,\n \"neutr\": 21599,\n \"biotechnology\": 21600,\n \"gase\": 21601,\n \"neurovascular\": 21602,\n \"naming\": 21603,\n \"tga\": 21604,\n \"transesophageal\": 21605,\n \"tubul\": 21606,\n \"flick\": 21607,\n \"reactors\": 21608,\n \"photoin\": 21609,\n \"reinforcing\": 21610,\n \"perovskite\": 21611,\n \"chlorhexidine\": 21612,\n \"wish\": 21613,\n \"oncologic\": 21614,\n \"extracting\": 21615,\n \"injectable\": 21616,\n \"fecund\": 21617,\n \"neurology\": 21618,\n \"polyposis\": 21619,\n \"gef\": 21620,\n \"royal\": 21621,\n \"initiates\": 21622,\n \"retire\": 21623,\n \"zw\": 21624,\n \"favoring\": 21625,\n \"demethyl\": 21626,\n \"##oniaz\": 21627,\n \"iranian\": 21628,\n \"patterned\": 21629,\n \"kyph\": 21630,\n \"continence\": 21631,\n \"##quinoline\": 21632,\n \"##azoline\": 21633,\n \"phosphorylase\": 21634,\n \"bangl\": 21635,\n \"##ths\": 21636,\n \"achievable\": 21637,\n \"dor\": 21638,\n \"vagina\": 21639,\n \"urgency\": 21640,\n \"infestation\": 21641,\n \"##reless\": 21642,\n \"370\": 21643,\n \"##ouns\": 21644,\n \"immunofluorescent\": 21645,\n \"aneuploidy\": 21646,\n \"periodically\": 21647,\n \"deae\": 21648,\n \"bangladesh\": 21649,\n \"abortions\": 21650,\n \"transmural\": 21651,\n \"prazosin\": 21652,\n \"##okines\": 21653,\n \"oldest\": 21654,\n \"anatomically\": 21655,\n \"subdural\": 21656,\n \"pathophysiologic\": 21657,\n \"buffalo\": 21658,\n \"midwives\": 21659,\n \"hypertrig\": 21660,\n \"##anone\": 21661,\n \"manipulating\": 21662,\n \"##eliness\": 21663,\n \"biodistribution\": 21664,\n \"millions\": 21665,\n \"smd\": 21666,\n \"intrinsically\": 21667,\n \"480\": 21668,\n \"mites\": 21669,\n \"hypoc\": 21670,\n \"##inflammation\": 21671,\n \"cem\": 21672,\n \"aspergill\": 21673,\n \"cushing\": 21674,\n \"transients\": 21675,\n \"contracted\": 21676,\n \"##bec\": 21677,\n \"digest\": 21678,\n \"pitfalls\": 21679,\n \"sildenafil\": 21680,\n \"electrochem\": 21681,\n \"hnf\": 21682,\n \"uranium\": 21683,\n \"colored\": 21684,\n \"##i1\": 21685,\n \"adn\": 21686,\n \"notew\": 21687,\n \"counteract\": 21688,\n \"electromyographic\": 21689,\n \"piezo\": 21690,\n \"instrumented\": 21691,\n \"spiking\": 21692,\n \"humor\": 21693,\n \"pgs\": 21694,\n \"##oton\": 21695,\n \"sos\": 21696,\n \"logical\": 21697,\n \"##okinin\": 21698,\n \"inferences\": 21699,\n \"acetylated\": 21700,\n \"avoids\": 21701,\n \"##iet\": 21702,\n \"spliced\": 21703,\n \"identities\": 21704,\n \"##oflavin\": 21705,\n \"biochemically\": 21706,\n \"nadp\": 21707,\n \"inductive\": 21708,\n \"supplies\": 21709,\n \"debilitating\": 21710,\n \"biomechan\": 21711,\n \"tertile\": 21712,\n \"bmc\": 21713,\n \"nitroglycer\": 21714,\n \"##methylation\": 21715,\n \"maneuver\": 21716,\n \"##pb\": 21717,\n \"drawbacks\": 21718,\n \"preg\": 21719,\n \"homosexual\": 21720,\n \"cholangitis\": 21721,\n \"sider\": 21722,\n \"##torh\": 21723,\n \"thiored\": 21724,\n \"228\": 21725,\n \"estrogenic\": 21726,\n \"compromising\": 21727,\n \"ed50\": 21728,\n \"presumptive\": 21729,\n \"kcn\": 21730,\n \"evacuation\": 21731,\n \"convex\": 21732,\n \"prrs\": 21733,\n \"##ultured\": 21734,\n \"fractal\": 21735,\n \"defensive\": 21736,\n \"indeterm\": 21737,\n \"cadavers\": 21738,\n \"photosystem\": 21739,\n \"crush\": 21740,\n \"isoniaz\": 21741,\n \"##oduoden\": 21742,\n \"sonic\": 21743,\n \"##osi\": 21744,\n \"annulus\": 21745,\n \"azathioprine\": 21746,\n \"baumannii\": 21747,\n \"knowing\": 21748,\n \"scru\": 21749,\n \"denaturing\": 21750,\n \"butter\": 21751,\n \"diat\": 21752,\n \"lymphangi\": 21753,\n \"burgd\": 21754,\n \"##κb\": 21755,\n \"ict\": 21756,\n \"ependym\": 21757,\n \"seemingly\": 21758,\n \"##uronium\": 21759,\n \"balf\": 21760,\n \"unbound\": 21761,\n \"adapting\": 21762,\n \"cba\": 21763,\n \"##azin\": 21764,\n \"phenotypically\": 21765,\n \"controller\": 21766,\n \"adenylyl\": 21767,\n \"##omib\": 21768,\n \"sorafenib\": 21769,\n \"hypophys\": 21770,\n \"lyme\": 21771,\n \"##operfusion\": 21772,\n \"radiologist\": 21773,\n \"wrong\": 21774,\n \"licensed\": 21775,\n \"transvers\": 21776,\n \"vsmc\": 21777,\n \"clip\": 21778,\n \"baroreflex\": 21779,\n \"niv\": 21780,\n \"tpp\": 21781,\n \"runoff\": 21782,\n \"abduction\": 21783,\n \"burgdor\": 21784,\n \"##ellin\": 21785,\n \"heterocyclic\": 21786,\n \"hydrolytic\": 21787,\n \"repolarization\": 21788,\n \"caen\": 21789,\n \"##ogastric\": 21790,\n \"prag\": 21791,\n \"merit\": 21792,\n \"gj\": 21793,\n \"phosphatases\": 21794,\n \"corners\": 21795,\n \"ptd\": 21796,\n \"pgi2\": 21797,\n \"##etus\": 21798,\n \"manufactured\": 21799,\n \"intensified\": 21800,\n \"metabolizing\": 21801,\n \"claimed\": 21802,\n \"hypercalcemia\": 21803,\n \"##omening\": 21804,\n \"unheal\": 21805,\n \"wireless\": 21806,\n \"uncoupling\": 21807,\n \"##centr\": 21808,\n \"##endothelial\": 21809,\n \"pach\": 21810,\n \"##h2o\": 21811,\n \"noteworthy\": 21812,\n \"holstein\": 21813,\n \"mof\": 21814,\n \"google\": 21815,\n \"##ucine\": 21816,\n \"dichloro\": 21817,\n \"cryptic\": 21818,\n \"##orptive\": 21819,\n \"potentiate\": 21820,\n \"##yer\": 21821,\n \"unco\": 21822,\n \"ags\": 21823,\n \"penal\": 21824,\n \"relied\": 21825,\n \"vsmcs\": 21826,\n \"leucocyte\": 21827,\n \"investigator\": 21828,\n \"##professional\": 21829,\n \"poc\": 21830,\n \"beneath\": 21831,\n \"sulcus\": 21832,\n \"##bw\": 21833,\n \"interconnected\": 21834,\n \"nrs\": 21835,\n \"pleomorphic\": 21836,\n \"oligomeric\": 21837,\n \"bacl\": 21838,\n \"globular\": 21839,\n \"burgdorferi\": 21840,\n \"jak2\": 21841,\n \"##orhabditis\": 21842,\n \"pns\": 21843,\n \"transurethral\": 21844,\n \"naa\": 21845,\n \"calcifications\": 21846,\n \"sulfoxide\": 21847,\n \"n3\": 21848,\n \"degranulation\": 21849,\n \"##ervical\": 21850,\n \"court\": 21851,\n \"heterozygotes\": 21852,\n \"##db\": 21853,\n \"##access\": 21854,\n \"245\": 21855,\n \"hypertriglycerid\": 21856,\n \"boots\": 21857,\n \"characterisation\": 21858,\n \"##adol\": 21859,\n \"##rology\": 21860,\n \"lett\": 21861,\n \"meter\": 21862,\n \"##cysteine\": 21863,\n \"3beta\": 21864,\n \"nci\": 21865,\n \"mbl\": 21866,\n \"shortest\": 21867,\n \"##folate\": 21868,\n \"isomerization\": 21869,\n \"pyrophosphate\": 21870,\n \"precipitate\": 21871,\n \"progressing\": 21872,\n \"tumorigenic\": 21873,\n \"so2\": 21874,\n \"b3lyp\": 21875,\n \"##aea\": 21876,\n \"fetoprotein\": 21877,\n \"##transplantation\": 21878,\n \"tailed\": 21879,\n \"##bear\": 21880,\n \"purpura\": 21881,\n \"trehalose\": 21882,\n \"mission\": 21883,\n \"##trid\": 21884,\n \"dihedral\": 21885,\n \"swab\": 21886,\n \"##omethane\": 21887,\n \"hcs\": 21888,\n \"00001\": 21889,\n \"##trex\": 21890,\n \"dac\": 21891,\n \"utilisation\": 21892,\n \"##ennial\": 21893,\n \"##ostosis\": 21894,\n \"budding\": 21895,\n \"buprenorphine\": 21896,\n \"lpr\": 21897,\n \"##otap\": 21898,\n \"glycated\": 21899,\n \"ale\": 21900,\n \"amend\": 21901,\n \"occlusions\": 21902,\n \"ki67\": 21903,\n \"geographically\": 21904,\n \"spasticity\": 21905,\n \"your\": 21906,\n \"sexuality\": 21907,\n \"underpinning\": 21908,\n \"incisor\": 21909,\n \"reinforce\": 21910,\n \"chronological\": 21911,\n \"##ran\": 21912,\n \"##immune\": 21913,\n \"phantoms\": 21914,\n \"##1a2\": 21915,\n \"##arth\": 21916,\n \"rivers\": 21917,\n \"thanks\": 21918,\n \"demonstrable\": 21919,\n \"##etries\": 21920,\n \"##sm\": 21921,\n \"adjuv\": 21922,\n \"hyponatre\": 21923,\n \"berg\": 21924,\n \"thyroiditis\": 21925,\n \"agn\": 21926,\n \"guarantee\": 21927,\n \"singular\": 21928,\n \"proto\": 21929,\n \"commiss\": 21930,\n \"##uma\": 21931,\n \"mlc\": 21932,\n \"biv\": 21933,\n \"gaseous\": 21934,\n \"vascularized\": 21935,\n \"myelodys\": 21936,\n \"##urities\": 21937,\n \"classifying\": 21938,\n \"connexin\": 21939,\n \"visiting\": 21940,\n \"ahi\": 21941,\n \"rhoa\": 21942,\n \"vivax\": 21943,\n \"polyt\": 21944,\n \"##af1\": 21945,\n \"fak\": 21946,\n \"##idines\": 21947,\n \"tan\": 21948,\n \"042\": 21949,\n \"dystrophin\": 21950,\n \"inefficient\": 21951,\n \"##180\": 21952,\n \"phenomenological\": 21953,\n \"recapit\": 21954,\n \"roi\": 21955,\n \"palmitoyl\": 21956,\n \"##cription\": 21957,\n \"pld\": 21958,\n \"appeal\": 21959,\n \"219\": 21960,\n \"convey\": 21961,\n \"isoniazid\": 21962,\n \"dysregulated\": 21963,\n \"facets\": 21964,\n \"assignments\": 21965,\n \"svm\": 21966,\n \"anteroposterior\": 21967,\n \"##odin\": 21968,\n \"transposon\": 21969,\n \"##amyl\": 21970,\n \"pgc\": 21971,\n \"allop\": 21972,\n \"washington\": 21973,\n \"##ovi\": 21974,\n \"rheological\": 21975,\n \"##cel\": 21976,\n \"club\": 21977,\n \"embolic\": 21978,\n \"##arabine\": 21979,\n \"website\": 21980,\n \"ay\": 21981,\n \"k2\": 21982,\n \"halo\": 21983,\n \"homing\": 21984,\n \"##orient\": 21985,\n \"collim\": 21986,\n \"##rogenesis\": 21987,\n \"serovar\": 21988,\n \"ryan\": 21989,\n \"consume\": 21990,\n \"delineation\": 21991,\n \"##ivirus\": 21992,\n \"necropsy\": 21993,\n \"##rosthetic\": 21994,\n \"##ospermia\": 21995,\n \"##burn\": 21996,\n \"fabricate\": 21997,\n \"euc\": 21998,\n \"grounds\": 21999,\n \"permitting\": 22000,\n \"player\": 22001,\n \"enzymatically\": 22002,\n \"sax\": 22003,\n \"ish\": 22004,\n \"novelty\": 22005,\n \"ssr\": 22006,\n \"##iper\": 22007,\n \"tut\": 22008,\n \"instructed\": 22009,\n \"village\": 22010,\n \"##arche\": 22011,\n \"unexplored\": 22012,\n \"styrene\": 22013,\n \"##balanced\": 22014,\n \"##q11\": 22015,\n \"artemis\": 22016,\n \"discontinuous\": 22017,\n \"histopathologically\": 22018,\n \"resins\": 22019,\n \"symbiotic\": 22020,\n \"peep\": 22021,\n \"##ondral\": 22022,\n \"categorization\": 22023,\n \"transv\": 22024,\n \"##urrent\": 22025,\n \"043\": 22026,\n \"##children\": 22027,\n \"anaesthetized\": 22028,\n \"stillbirth\": 22029,\n \"streptomycin\": 22030,\n \"postp\": 22031,\n \"afterwards\": 22032,\n \"isothermal\": 22033,\n \"##00000000\": 22034,\n \"valgus\": 22035,\n \"sjogr\": 22036,\n \"##bv\": 22037,\n \"##n3\": 22038,\n \"genbank\": 22039,\n \"ryr\": 22040,\n \"##ectoris\": 22041,\n \"nonex\": 22042,\n \"234\": 22043,\n \"leucocytes\": 22044,\n \"externally\": 22045,\n \"abundances\": 22046,\n \"##atids\": 22047,\n \"pept\": 22048,\n \"joining\": 22049,\n \"nyha\": 22050,\n \"##yled\": 22051,\n \"achilles\": 22052,\n \"pvr\": 22053,\n \"neuraminidase\": 22054,\n \"grape\": 22055,\n \"urticaria\": 22056,\n \"##ourished\": 22057,\n \"certainly\": 22058,\n \"##ometabolic\": 22059,\n \"##ematous\": 22060,\n \"microglobulin\": 22061,\n \"taught\": 22062,\n \"aversive\": 22063,\n \"multicellular\": 22064,\n \"##293\": 22065,\n \"##romegal\": 22066,\n \"##osable\": 22067,\n \"involunt\": 22068,\n \"##activated\": 22069,\n \"cd56\": 22070,\n \"##esin\": 22071,\n \"albuminuria\": 22072,\n \"contraindications\": 22073,\n \"relieved\": 22074,\n \"eliminates\": 22075,\n \"##lc\": 22076,\n \"architectures\": 22077,\n \"certification\": 22078,\n \"nicu\": 22079,\n \"keyword\": 22080,\n \"mpp\": 22081,\n \"##ffer\": 22082,\n \"transcriptionally\": 22083,\n \"orifice\": 22084,\n \"invading\": 22085,\n \"cannulation\": 22086,\n \"v3\": 22087,\n \"synchro\": 22088,\n \"heritable\": 22089,\n \"negoti\": 22090,\n \"chat\": 22091,\n \"esophagitis\": 22092,\n \"##ensory\": 22093,\n \"hgh\": 22094,\n \"broadband\": 22095,\n \"hydroxymethyl\": 22096,\n \"ensures\": 22097,\n \"tenderness\": 22098,\n \"ncr\": 22099,\n \"calcane\": 22100,\n \"endodontic\": 22101,\n \"sps\": 22102,\n \"##ca1\": 22103,\n \"phosphatidylserine\": 22104,\n \"sjogren\": 22105,\n \"6th\": 22106,\n \"cd19\": 22107,\n \"api\": 22108,\n \"acetaldehyde\": 22109,\n \"visibility\": 22110,\n \"241\": 22111,\n \"shh\": 22112,\n \"stomatal\": 22113,\n \"octreotide\": 22114,\n \"dephosphorylation\": 22115,\n \"prick\": 22116,\n \"hospitalisation\": 22117,\n \"auxiliary\": 22118,\n \"##igenin\": 22119,\n \"rit\": 22120,\n \"incar\": 22121,\n \"innerv\": 22122,\n \"neuromod\": 22123,\n \"affordable\": 22124,\n \"nanorods\": 22125,\n \"hun\": 22126,\n \"immunosuppress\": 22127,\n \"thai\": 22128,\n \"forth\": 22129,\n \"##ochondral\": 22130,\n \"perfusate\": 22131,\n \"##tri\": 22132,\n \"stn\": 22133,\n \"acclimation\": 22134,\n \"vitell\": 22135,\n \"avr\": 22136,\n \"##ocortex\": 22137,\n \"scrutin\": 22138,\n \"bats\": 22139,\n \"##onol\": 22140,\n \"midgut\": 22141,\n \"bottlen\": 22142,\n \"mmt\": 22143,\n \"sensorineural\": 22144,\n \"heel\": 22145,\n \"telemedicine\": 22146,\n \"aggressiveness\": 22147,\n \"##radi\": 22148,\n \"isoenzyme\": 22149,\n \"incap\": 22150,\n \"##being\": 22151,\n \"glucoside\": 22152,\n \"5s\": 22153,\n \"coul\": 22154,\n \"aii\": 22155,\n \"##ternal\": 22156,\n \"classroom\": 22157,\n \"##oting\": 22158,\n \"immunoassays\": 22159,\n \"microorganism\": 22160,\n \"directing\": 22161,\n \"morphometry\": 22162,\n \"urease\": 22163,\n \"diphther\": 22164,\n \"myoblasts\": 22165,\n \"##opolymer\": 22166,\n \"trpv1\": 22167,\n \"rotations\": 22168,\n \"postulate\": 22169,\n \"##acyt\": 22170,\n \"news\": 22171,\n \"locoregional\": 22172,\n \"breakfast\": 22173,\n \"sulfonyl\": 22174,\n \"frustr\": 22175,\n 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22221,\n \"slip\": 22222,\n \"sant\": 22223,\n \"maxima\": 22224,\n \"##zomib\": 22225,\n \"##othi\": 22226,\n \"229\": 22227,\n \"##hog\": 22228,\n \"deaminase\": 22229,\n \"cb1\": 22230,\n \"ambul\": 22231,\n \"miniature\": 22232,\n \"conventionally\": 22233,\n \"imipenem\": 22234,\n \"interferes\": 22235,\n \"##verted\": 22236,\n \"magnitudes\": 22237,\n \"##tress\": 22238,\n \"triterp\": 22239,\n \"fts\": 22240,\n \"##omplex\": 22241,\n \"conclusive\": 22242,\n \"wd\": 22243,\n \"##avicular\": 22244,\n \"adhered\": 22245,\n \"clarithromycin\": 22246,\n \"polyg\": 22247,\n \"##ucting\": 22248,\n \"lactis\": 22249,\n \"rage\": 22250,\n \"##porter\": 22251,\n \"1966\": 22252,\n \"##fluorescence\": 22253,\n \"laevis\": 22254,\n \"##pot\": 22255,\n \"##osh\": 22256,\n \"molecularly\": 22257,\n \"rectang\": 22258,\n \"ihd\": 22259,\n \"vasoconstrictor\": 22260,\n \"##ivudine\": 22261,\n \"mcd\": 22262,\n \"kilob\": 22263,\n \"ancestor\": 22264,\n \"quantitated\": 22265,\n 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22443,\n \"laminar\": 22444,\n \"mainstay\": 22445,\n \"facilitators\": 22446,\n \"mine\": 22447,\n \"##2r\": 22448,\n \"urodynamic\": 22449,\n \"ner\": 22450,\n \"forel\": 22451,\n \"nadir\": 22452,\n \"##cryst\": 22453,\n \"clath\": 22454,\n \"17a\": 22455,\n \"sedative\": 22456,\n \"compass\": 22457,\n \"##missions\": 22458,\n \"bioassays\": 22459,\n \"fluorine\": 22460,\n \"ginseng\": 22461,\n \"tampon\": 22462,\n \"ccd\": 22463,\n \"stump\": 22464,\n \"mthfr\": 22465,\n \"ui\": 22466,\n \"universally\": 22467,\n \"gynecological\": 22468,\n \"indexed\": 22469,\n \"nf1\": 22470,\n \"entang\": 22471,\n \"serially\": 22472,\n \"248\": 22473,\n \"agend\": 22474,\n \"ceftriax\": 22475,\n \"urs\": 22476,\n \"endarterectomy\": 22477,\n \"pleu\": 22478,\n \"hypothermic\": 22479,\n \"administer\": 22480,\n \"fluidity\": 22481,\n \"riboflavin\": 22482,\n \"interspecific\": 22483,\n \"thioredoxin\": 22484,\n \"extubation\": 22485,\n \"##onecrosis\": 22486,\n \"enhancers\": 22487,\n \"igh\": 22488,\n \"herbicide\": 22489,\n \"town\": 22490,\n \"draft\": 22491,\n \"floating\": 22492,\n \"acknowledged\": 22493,\n \"##alasin\": 22494,\n \"kre\": 22495,\n \"f3\": 22496,\n \"matr\": 22497,\n \"nonrandom\": 22498,\n \"amalg\": 22499,\n \"##acholine\": 22500,\n \"originates\": 22501,\n \"immersed\": 22502,\n \"gm1\": 22503,\n \"mehg\": 22504,\n \"eigen\": 22505,\n \"replacements\": 22506,\n \"carboxylase\": 22507,\n \"##ifferentiation\": 22508,\n \"##ystitis\": 22509,\n \"youths\": 22510,\n \"endocardial\": 22511,\n \"##ostigmine\": 22512,\n \"cycload\": 22513,\n \"nim\": 22514,\n \"lactone\": 22515,\n \"##opram\": 22516,\n \"corroborated\": 22517,\n \"egr\": 22518,\n \"##fv\": 22519,\n \"supervis\": 22520,\n \"##romed\": 22521,\n \"##odomain\": 22522,\n \"arte\": 22523,\n \"enterica\": 22524,\n \"##3t3\": 22525,\n \"antidiabetic\": 22526,\n \"##behavi\": 22527,\n \"sid\": 22528,\n \"mbc\": 22529,\n \"impurities\": 22530,\n \"adenoviral\": 22531,\n \"##oproph\": 22532,\n \"indeterminate\": 22533,\n \"perone\": 22534,\n \"strugg\": 22535,\n \"landf\": 22536,\n \"pear\": 22537,\n \"unavail\": 22538,\n \"lyophil\": 22539,\n \"pant\": 22540,\n \"##arius\": 22541,\n \"refine\": 22542,\n \"syringe\": 22543,\n \"##oning\": 22544,\n \"##rice\": 22545,\n \"##italopram\": 22546,\n \"cory\": 22547,\n \"hapt\": 22548,\n \"pthr\": 22549,\n \"antimalarial\": 22550,\n \"##arded\": 22551,\n \"amh\": 22552,\n \"##welling\": 22553,\n \"ngs\": 22554,\n \"adventi\": 22555,\n \"##tite\": 22556,\n \"usp\": 22557,\n \"##afted\": 22558,\n \"325\": 22559,\n \"rps\": 22560,\n \"perpetr\": 22561,\n \"caspases\": 22562,\n \"appreciation\": 22563,\n \"genu\": 22564,\n \"strab\": 22565,\n \"hypermethylation\": 22566,\n \"##idian\": 22567,\n \"juxt\": 22568,\n \"##s2\": 22569,\n \"kup\": 22570,\n \"carbonic\": 22571,\n \"tracked\": 22572,\n \"kiss\": 22573,\n \"meteor\": 22574,\n \"intratumoral\": 22575,\n \"accreditation\": 22576,\n \"##antes\": 22577,\n \"trying\": 22578,\n \"preload\": 22579,\n \"perforated\": 22580,\n \"party\": 22581,\n \"blasts\": 22582,\n \"quartiles\": 22583,\n \"fractured\": 22584,\n \"disinf\": 22585,\n \"singapore\": 22586,\n \"antitr\": 22587,\n \"ductus\": 22588,\n \"nonhuman\": 22589,\n \"dioxin\": 22590,\n \"cholecystitis\": 22591,\n \"repairs\": 22592,\n \"door\": 22593,\n \"lifelong\": 22594,\n \"aro\": 22595,\n \"swollen\": 22596,\n \"modifier\": 22597,\n \"proceeding\": 22598,\n \"hba\": 22599,\n \"mos2\": 22600,\n \"ceftriaxone\": 22601,\n \"humerus\": 22602,\n \"offs\": 22603,\n \"mutagenicity\": 22604,\n \"mediastinum\": 22605,\n \"potencies\": 22606,\n \"willi\": 22607,\n \"ht1a\": 22608,\n \"latino\": 22609,\n \"estimations\": 22610,\n \"diffusivity\": 22611,\n \"##atial\": 22612,\n \"condyle\": 22613,\n \"##tilb\": 22614,\n \"##oronary\": 22615,\n \"fcs\": 22616,\n \"elaborate\": 22617,\n \"abbre\": 22618,\n \"weaknesses\": 22619,\n \"b4\": 22620,\n \"cus\": 22621,\n \"proprio\": 22622,\n \"##ureter\": 22623,\n 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22667,\n \"demethylation\": 22668,\n \"sulfameth\": 22669,\n \"##ih\": 22670,\n \"quadruplex\": 22671,\n \"##acoustic\": 22672,\n \"unprot\": 22673,\n \"exercised\": 22674,\n \"hydroxyvitamin\": 22675,\n \"abbrevi\": 22676,\n \"##jd\": 22677,\n \"seeks\": 22678,\n \"encephalomyelitis\": 22679,\n \"duplicate\": 22680,\n \"protonation\": 22681,\n \"##iors\": 22682,\n \"glasses\": 22683,\n \"victor\": 22684,\n \"##bos\": 22685,\n \"synchrony\": 22686,\n \"243\": 22687,\n \"coadmin\": 22688,\n \"fra\": 22689,\n \"shigella\": 22690,\n \"##ocalization\": 22691,\n \"tcc\": 22692,\n \"neurofil\": 22693,\n \"aforem\": 22694,\n \"metallothione\": 22695,\n \"atria\": 22696,\n \"liability\": 22697,\n \"aforementioned\": 22698,\n \"angio\": 22699,\n \"030\": 22700,\n \"odc\": 22701,\n \"kenya\": 22702,\n \"microflora\": 22703,\n \"mdr1\": 22704,\n \"coales\": 22705,\n \"anoph\": 22706,\n \"##agal\": 22707,\n \"duck\": 22708,\n \"heights\": 22709,\n \"maltreatment\": 22710,\n \"marm\": 22711,\n \"cholangiocarcinoma\": 22712,\n \"##antigens\": 22713,\n \"##oporphyrin\": 22714,\n \"##ethane\": 22715,\n \"044\": 22716,\n \"amyotrophic\": 22717,\n \"##entilation\": 22718,\n \"retinoid\": 22719,\n \"1972\": 22720,\n \"pwv\": 22721,\n \"oncologists\": 22722,\n \"mend\": 22723,\n \"##ypti\": 22724,\n \"##ocentesis\": 22725,\n \"colomb\": 22726,\n \"mvd\": 22727,\n \"hedgehog\": 22728,\n \"##win\": 22729,\n \"##ostal\": 22730,\n \"hyaluronan\": 22731,\n \"hallucinations\": 22732,\n \"cdh\": 22733,\n \"appendix\": 22734,\n \"nanog\": 22735,\n \"131i\": 22736,\n \"lacun\": 22737,\n \"rbp\": 22738,\n \"##rowing\": 22739,\n \"4b\": 22740,\n \"orthostatic\": 22741,\n \"gus\": 22742,\n \"beverage\": 22743,\n \"227\": 22744,\n \"##apical\": 22745,\n \"gynecology\": 22746,\n \"cholinesterase\": 22747,\n \"homozygotes\": 22748,\n \"##odyst\": 22749,\n \"yers\": 22750,\n \"15n\": 22751,\n \"##126\": 22752,\n \"incarcer\": 22753,\n \"fbs\": 22754,\n \"rer\": 22755,\n \"##ylv\": 22756,\n \"telev\": 22757,\n \"concerted\": 22758,\n \"##adaptive\": 22759,\n \"powders\": 22760,\n \"hyponatremia\": 22761,\n \"zh\": 22762,\n \"cords\": 22763,\n \"interindividual\": 22764,\n \"shunting\": 22765,\n \"vigilance\": 22766,\n \"audio\": 22767,\n \"astrogl\": 22768,\n \"ferrous\": 22769,\n \"242\": 22770,\n \"tall\": 22771,\n \"brucella\": 22772,\n \"hib\": 22773,\n \"thiamine\": 22774,\n \"exceptionally\": 22775,\n \"1ra\": 22776,\n \"viscous\": 22777,\n \"symbion\": 22778,\n \"flare\": 22779,\n \"##oplegia\": 22780,\n \"proficiency\": 22781,\n \"keratocon\": 22782,\n \"##enoids\": 22783,\n \"##the\": 22784,\n \"stear\": 22785,\n \"ringer\": 22786,\n \"unhealthy\": 22787,\n \"##orbed\": 22788,\n \"incidentally\": 22789,\n \"hots\": 22790,\n \"##aters\": 22791,\n \"quail\": 22792,\n \"gp120\": 22793,\n \"##arterial\": 22794,\n \"byst\": 22795,\n \"alkylation\": 22796,\n \"gdnf\": 22797,\n \"ionized\": 22798,\n \"##alkyl\": 22799,\n \"##onscious\": 22800,\n \"##apsules\": 22801,\n 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\"##ospatial\": 22845,\n \"levo\": 22846,\n \"nucleophilic\": 22847,\n \"lrp\": 22848,\n \"##506\": 22849,\n \"cinnam\": 22850,\n \"luminance\": 22851,\n \"alfa\": 22852,\n \"subgen\": 22853,\n \"lacc\": 22854,\n \"adjuvants\": 22855,\n \"252\": 22856,\n \"ici\": 22857,\n \"pread\": 22858,\n \"seropositivity\": 22859,\n \"##bearing\": 22860,\n \"fron\": 22861,\n \"nonl\": 22862,\n \"hyperbaric\": 22863,\n \"cardiogenic\": 22864,\n \"cmt\": 22865,\n \"##iclovir\": 22866,\n \"cardioprotective\": 22867,\n \"##hard\": 22868,\n \"##critical\": 22869,\n \"osas\": 22870,\n \"inactivity\": 22871,\n \"bottle\": 22872,\n \"azithromycin\": 22873,\n \"transcriptomic\": 22874,\n \"scleroderma\": 22875,\n \"nomen\": 22876,\n \"advocate\": 22877,\n \"hourly\": 22878,\n \"stokes\": 22879,\n \"##opathogenic\": 22880,\n \"incisions\": 22881,\n \"errone\": 22882,\n \"cyp2d6\": 22883,\n \"carboxymethyl\": 22884,\n \"clarity\": 22885,\n \"astrocytic\": 22886,\n \"neocortex\": 22887,\n \"bacteroides\": 22888,\n \"adenomatous\": 22889,\n \"cci\": 22890,\n \"recanalization\": 22891,\n \"disks\": 22892,\n \"densely\": 22893,\n \"##olipin\": 22894,\n \"lfa\": 22895,\n \"nanowire\": 22896,\n \"dexmed\": 22897,\n \"methacholine\": 22898,\n \"tetramer\": 22899,\n \"petroleum\": 22900,\n \"asymmetrical\": 22901,\n \"fibrillar\": 22902,\n \"##eight\": 22903,\n \"inaccurate\": 22904,\n \"exhaled\": 22905,\n \"1970s\": 22906,\n \"##othermic\": 22907,\n \"fe3o4\": 22908,\n \"clind\": 22909,\n \"ketones\": 22910,\n \"runners\": 22911,\n \"pcm\": 22912,\n \"magnification\": 22913,\n \"vacuolar\": 22914,\n \"dipolar\": 22915,\n \"antarctic\": 22916,\n \"cine\": 22917,\n \"synonymous\": 22918,\n \"hydroxyproline\": 22919,\n \"prevalences\": 22920,\n \"pragmatic\": 22921,\n \"hysteresis\": 22922,\n \"triangular\": 22923,\n \"glur\": 22924,\n \"shortcom\": 22925,\n \"languages\": 22926,\n \"crosstalk\": 22927,\n \"syntheses\": 22928,\n \"polyneu\": 22929,\n \"shortcomings\": 22930,\n \"assault\": 22931,\n \"##ecies\": 22932,\n \"rheumatology\": 22933,\n \"##arietal\": 22934,\n \"acetylgluc\": 22935,\n \"elaborated\": 22936,\n \"bend\": 22937,\n \"dcis\": 22938,\n \"mesenchym\": 22939,\n \"mechanistically\": 22940,\n \"##exy\": 22941,\n \"replicating\": 22942,\n \"hcy\": 22943,\n \"thicker\": 22944,\n \"temporarily\": 22945,\n \"dyskinesia\": 22946,\n \"dexmedetomidine\": 22947,\n \"chb\": 22948,\n \"intraves\": 22949,\n \"shuttle\": 22950,\n \"viewpoint\": 22951,\n \"privacy\": 22952,\n \"sarcopenia\": 22953,\n \"decis\": 22954,\n \"postex\": 22955,\n \"tgfβ\": 22956,\n \"##oblastomas\": 22957,\n \"eur\": 22958,\n \"imipramine\": 22959,\n \"##reshold\": 22960,\n \"cytolytic\": 22961,\n \"cargo\": 22962,\n \"≥3\": 22963,\n \"##cb\": 22964,\n \"ictal\": 22965,\n \"##igel\": 22966,\n \"stoichiometric\": 22967,\n \"##ongru\": 22968,\n \"lncap\": 22969,\n \"cd1\": 22970,\n \"multifacet\": 22971,\n \"sns\": 22972,\n \"thiols\": 22973,\n \"misdiagnosed\": 22974,\n \"antiphospholipid\": 22975,\n \"trus\": 22976,\n \"furn\": 22977,\n \"255\": 22978,\n \"##ecium\": 22979,\n \"fulm\": 22980,\n \"mitigation\": 22981,\n \"benzoate\": 22982,\n \"##116\": 22983,\n \"rnfl\": 22984,\n \"u937\": 22985,\n \"acceptors\": 22986,\n \"11c\": 22987,\n \"anchorage\": 22988,\n \"bisphosphonates\": 22989,\n \"involuntary\": 22990,\n \"rx\": 22991,\n \"overproduction\": 22992,\n \"ryanodine\": 22993,\n \"chagas\": 22994,\n \"absorbing\": 22995,\n \"specialization\": 22996,\n \"##azid\": 22997,\n \"calv\": 22998,\n \"multiplicity\": 22999,\n \"##riers\": 23000,\n \"##etting\": 23001,\n \"breakage\": 23002,\n \"shielding\": 23003,\n \"formulate\": 23004,\n \"questionable\": 23005,\n \"##outs\": 23006,\n \"clindamycin\": 23007,\n \"edible\": 23008,\n \"pione\": 23009,\n \"##agle\": 23010,\n \"metamorph\": 23011,\n \"advancements\": 23012,\n \"##trexone\": 23013,\n \"nsp\": 23014,\n \"actinomycin\": 23015,\n \"ipr\": 23016,\n \"crowns\": 23017,\n \"implicating\": 23018,\n \"dentine\": 23019,\n \"recalc\": 23020,\n \"demineral\": 23021,\n \"celecoxib\": 23022,\n \"##ytoplasmic\": 23023,\n \"singly\": 23024,\n \"quotient\": 23025,\n \"soccer\": 23026,\n \"mold\": 23027,\n \"internally\": 23028,\n \"radioiod\": 23029,\n \"multifaceted\": 23030,\n \"isth\": 23031,\n \"operates\": 23032,\n \"electronics\": 23033,\n \"oocysts\": 23034,\n \"##epi\": 23035,\n \"##ohyd\": 23036,\n \"externalizing\": 23037,\n \"comfortable\": 23038,\n \"russian\": 23039,\n \"nymph\": 23040,\n \"epilept\": 23041,\n \"##architect\": 23042,\n \"sce\": 23043,\n \"##erea\": 23044,\n \"aedes\": 23045,\n \"nms\": 23046,\n \"dehis\": 23047,\n \"jointly\": 23048,\n \"syncytial\": 23049,\n \"infinite\": 23050,\n \"endos\": 23051,\n \"isozyme\": 23052,\n \"##osmotic\": 23053,\n \"covariate\": 23054,\n \"isomerase\": 23055,\n \"manufacture\": 23056,\n \"##esartan\": 23057,\n \"259\": 23058,\n \"##onated\": 23059,\n \"casting\": 23060,\n \"brand\": 23061,\n \"schistosoma\": 23062,\n \"fumigatus\": 23063,\n \"bifunctional\": 23064,\n \"##fused\": 23065,\n \"littermates\": 23066,\n \"0003\": 23067,\n \"photolysis\": 23068,\n \"crab\": 23069,\n \"ohda\": 23070,\n \"##ifolia\": 23071,\n \"strengthened\": 23072,\n \"darkness\": 23073,\n \"michaelis\": 23074,\n \"imperfect\": 23075,\n \"polyaden\": 23076,\n \"rech\": 23077,\n \"##inately\": 23078,\n \"aerosols\": 23079,\n \"##eland\": 23080,\n \"mushroom\": 23081,\n \"##cessing\": 23082,\n \"pressing\": 23083,\n \"vagus\": 23084,\n \"snails\": 23085,\n \"##h3\": 23086,\n \"##nts\": 23087,\n \"265\": 23088,\n \"multiplication\": 23089,\n \"gamet\": 23090,\n \"##astigotes\": 23091,\n \"iri\": 23092,\n \"inductively\": 23093,\n \"##ivial\": 23094,\n \"##iptyline\": 23095,\n \"condylar\": 23096,\n \"plp\": 23097,\n \"gig\": 23098,\n \"##rotoxin\": 23099,\n \"columbia\": 23100,\n \"ncc\": 23101,\n \"shel\": 23102,\n \"intervening\": 23103,\n \"nomencl\": 23104,\n \"pend\": 23105,\n \"borte\": 23106,\n \"##reported\": 23107,\n \"parall\": 23108,\n \"tricyclic\": 23109,\n \"cbp\": 23110,\n \"suspect\": 23111,\n \"meningococcal\": 23112,\n \"peptidoglycan\": 23113,\n \"tetrod\": 23114,\n \"##fu\": 23115,\n \"##acological\": 23116,\n \"c1q\": 23117,\n \"##aval\": 23118,\n \"incongru\": 23119,\n \"phagocytes\": 23120,\n \"hac\": 23121,\n \"emergencies\": 23122,\n \"denitrification\": 23123,\n \"bortezomib\": 23124,\n \"##lational\": 23125,\n \"neuroleptic\": 23126,\n \"peroxyn\": 23127,\n \"endings\": 23128,\n \"interdep\": 23129,\n \"cst\": 23130,\n \"diacylglycerol\": 23131,\n \"simian\": 23132,\n \"enth\": 23133,\n \"biob\": 23134,\n \"1s\": 23135,\n \"transvaginal\": 23136,\n \"fourfold\": 23137,\n \"blockage\": 23138,\n \"retains\": 23139,\n \"lactoferrin\": 23140,\n \"nem\": 23141,\n \"neuropathological\": 23142,\n \"neuroinflammation\": 23143,\n \"dyslex\": 23144,\n \"dsrna\": 23145,\n \"triiodothyronine\": 23146,\n \"##oagulation\": 23147,\n \"##icus\": 23148,\n \"coordinating\": 23149,\n \"hbo\": 23150,\n \"nut\": 23151,\n \"##itidine\": 23152,\n \"underweight\": 23153,\n \"baclofen\": 23154,\n \"chances\": 23155,\n \"preparedness\": 23156,\n \"##ealth\": 23157,\n \"phal\": 23158,\n \"telomeric\": 23159,\n \"concussion\": 23160,\n \"##junctiv\": 23161,\n \"##eno\": 23162,\n \"##atography\": 23163,\n \"gor\": 23164,\n \"juveniles\": 23165,\n \"polish\": 23166,\n \"neurosurgery\": 23167,\n \"deacetylase\": 23168,\n \"accessions\": 23169,\n \"##port\": 23170,\n \"##erves\": 23171,\n \"flocc\": 23172,\n \"cognitively\": 23173,\n \"bronchiolitis\": 23174,\n \"deactivation\": 23175,\n \"exudate\": 23176,\n \"periodicals\": 23177,\n \"multitude\": 23178,\n \"fenes\": 23179,\n \"metoprolol\": 23180,\n \"edent\": 23181,\n \"fixing\": 23182,\n \"##rad\": 23183,\n \"reassess\": 23184,\n \"cd133\": 23185,\n \"renders\": 23186,\n \"pdc\": 23187,\n \"duplicated\": 23188,\n \"##adate\": 23189,\n \"##odone\": 23190,\n \"251\": 23191,\n \"liz\": 23192,\n \"##ituric\": 23193,\n \"##hydroxy\": 23194,\n \"##akia\": 23195,\n \"pork\": 23196,\n \"claud\": 23197,\n \"f4\": 23198,\n \"##apheresis\": 23199,\n \"##inesis\": 23200,\n \"nucleosome\": 23201,\n \"spt\": 23202,\n \"denv\": 23203,\n \"flip\": 23204,\n \"accessing\": 23205,\n \"epididymis\": 23206,\n \"rgo\": 23207,\n \"dce\": 23208,\n \"tj\": 23209,\n \"cbz\": 23210,\n \"##tinib\": 23211,\n \"senile\": 23212,\n \"quiet\": 23213,\n \"##etermined\": 23214,\n \"fulminant\": 23215,\n \"ovip\": 23216,\n \"ultrafast\": 23217,\n \"nonalcoholic\": 23218,\n \"narcotic\": 23219,\n \"dating\": 23220,\n \"##adecyl\": 23221,\n \"266\": 23222,\n \"nmdar\": 23223,\n \"misle\": 23224,\n \"vibrations\": 23225,\n \"usability\": 23226,\n \"##capes\": 23227,\n \"melt\": 23228,\n \"neurobehavi\": 23229,\n \"startle\": 23230,\n \"tetrodotoxin\": 23231,\n \"save\": 23232,\n \"monocytic\": 23233,\n \"villous\": 23234,\n \"237\": 23235,\n \"gratings\": 23236,\n \"hemangioma\": 23237,\n \"disparate\": 23238,\n \"perturbed\": 23239,\n \"discourse\": 23240,\n \"partum\": 23241,\n \"specify\": 23242,\n \"rooms\": 23243,\n \"formate\": 23244,\n \"dentist\": 23245,\n \"scopus\": 23246,\n \"##ancerous\": 23247,\n \"nanosheets\": 23248,\n \"sis\": 23249,\n \"allergies\": 23250,\n \"polyamines\": 23251,\n \"##lm\": 23252,\n \"researches\": 23253,\n \"hca\": 23254,\n \"characterizes\": 23255,\n \"transducers\": 23256,\n \"vocabular\": 23257,\n \"emit\": 23258,\n \"predominated\": 23259,\n \"##meter\": 23260,\n \"facs\": 23261,\n \"iud\": 23262,\n \"biomaterial\": 23263,\n \"qsar\": 23264,\n \"relying\": 23265,\n \"aspergillosis\": 23266,\n \"encompasses\": 23267,\n \"##menting\": 23268,\n \"unrel\": 23269,\n \"##oremediation\": 23270,\n \"lithotr\": 23271,\n \"capturing\": 23272,\n \"6m\": 23273,\n \"suite\": 23274,\n \"cort\": 23275,\n \"curricula\": 23276,\n \"##alog\": 23277,\n \"cvs\": 23278,\n \"naoh\": 23279,\n \"dilemma\": 23280,\n \"315\": 23281,\n \"shocks\": 23282,\n \"sublethal\": 23283,\n \"transist\": 23284,\n \"crass\": 23285,\n \"laid\": 23286,\n \"##graphs\": 23287,\n \"cacl\": 23288,\n \"##opharynx\": 23289,\n \"grows\": 23290,\n \"hydron\": 23291,\n \"laterally\": 23292,\n \"underlies\": 23293,\n \"anesthesiologists\": 23294,\n \"carrageenan\": 23295,\n \"##opically\": 23296,\n \"chlorinated\": 23297,\n \"enantioselective\": 23298,\n \"segregated\": 23299,\n \"arthrodesis\": 23300,\n \"depolarizing\": 23301,\n \"scotland\": 23302,\n \"charco\": 23303,\n \"shunts\": 23304,\n \"##transplant\": 23305,\n \"##1p\": 23306,\n \"vortex\": 23307,\n \"arteriography\": 23308,\n \"thawing\": 23309,\n \"polyelectroly\": 23310,\n \"##inum\": 23311,\n \"conspicuous\": 23312,\n \"jh\": 23313,\n \"sight\": 23314,\n \"rcbf\": 23315,\n \"booster\": 23316,\n \"triacylglycerol\": 23317,\n \"immunoreg\": 23318,\n \"rhodopsin\": 23319,\n \"##orrhizal\": 23320,\n \"toxoplasmosis\": 23321,\n \"lamivudine\": 23322,\n \"##ivir\": 23323,\n \"speculated\": 23324,\n \"##romazine\": 23325,\n \"projecting\": 23326,\n \"argentina\": 23327,\n \"dma\": 23328,\n \"indones\": 23329,\n \"lmwh\": 23330,\n \"##avidin\": 23331,\n \"spaced\": 23332,\n \"pedigree\": 23333,\n \"farming\": 23334,\n \"bubbles\": 23335,\n \"cancellous\": 23336,\n \"convulsions\": 23337,\n \"belt\": 23338,\n \"##edoch\": 23339,\n \"endocytic\": 23340,\n \"cyp1a1\": 23341,\n \"##ophyt\": 23342,\n \"excurs\": 23343,\n \"denervated\": 23344,\n \"weaned\": 23345,\n \"phr\": 23346,\n \"monitors\": 23347,\n \"adamts\": 23348,\n \"##uer\": 23349,\n \"wakefulness\": 23350,\n \"##rosterone\": 23351,\n \"circums\": 23352,\n \"pediatricians\": 23353,\n \"pdl\": 23354,\n \"myasthenia\": 23355,\n \"lind\": 23356,\n \"spill\": 23357,\n \"wheezing\": 23358,\n \"columnar\": 23359,\n \"glucuronidase\": 23360,\n \"territories\": 23361,\n \"##arrhythm\": 23362,\n \"nanomolar\": 23363,\n \"causality\": 23364,\n \"##oven\": 23365,\n \"colectomy\": 23366,\n \"hemorrhages\": 23367,\n \"ghz\": 23368,\n \"##5b\": 23369,\n \"inbreeding\": 23370,\n \"##ellites\": 23371,\n \"##ectant\": 23372,\n \"suckling\": 23373,\n \"neuroanat\": 23374,\n \"infra\": 23375,\n \"##omponent\": 23376,\n \"absc\": 23377,\n \"vasodilatation\": 23378,\n \"##tins\": 23379,\n \"pav\": 23380,\n \"##omel\": 23381,\n \"milder\": 23382,\n \"myotubes\": 23383,\n \"##alic\": 23384,\n \"##butamol\": 23385,\n \"wg\": 23386,\n \"soldi\": 23387,\n \"torr\": 23388,\n \"aegypti\": 23389,\n \"cscs\": 23390,\n \"affords\": 23391,\n \"##ht\": 23392,\n \"hypercapnia\": 23393,\n \"tamponade\": 23394,\n \"nonadh\": 23395,\n \"supras\": 23396,\n \"vign\": 23397,\n \"##encl\": 23398,\n \"##space\": 23399,\n \"physico\": 23400,\n \"zirconia\": 23401,\n \"pthrp\": 23402,\n \"intentional\": 23403,\n \"kev\": 23404,\n \"pediatrics\": 23405,\n \"##lete\": 23406,\n \"valproate\": 23407,\n \"syll\": 23408,\n \"thawed\": 23409,\n \"##thio\": 23410,\n \"##quat\": 23411,\n \"paa\": 23412,\n \"inflammasome\": 23413,\n \"##lymph\": 23414,\n \"irritable\": 23415,\n \"ther\": 23416,\n \"calving\": 23417,\n \"##edral\": 23418,\n \"bimodal\": 23419,\n \"pneumonitis\": 23420,\n \"##eic\": 23421,\n \"##olon\": 23422,\n \"globe\": 23423,\n \"sms\": 23424,\n \"allodynia\": 23425,\n \"fura\": 23426,\n \"##opeptides\": 23427,\n \"##ogloss\": 23428,\n \"##uters\": 23429,\n \"ipc\": 23430,\n \"inulin\": 23431,\n \"genit\": 23432,\n \"equator\": 23433,\n \"msv\": 23434,\n \"thoracoscopic\": 23435,\n \"##rolateral\": 23436,\n \"246\": 23437,\n \"rrs\": 23438,\n \"conceived\": 23439,\n \"reentr\": 23440,\n \"##yletic\": 23441,\n \"unprotected\": 23442,\n \"dione\": 23443,\n \"##oty\": 23444,\n \"qaly\": 23445,\n \"##ocs\": 23446,\n \"##sil\": 23447,\n \"stands\": 23448,\n \"salbutamol\": 23449,\n \"##pregnant\": 23450,\n \"peroxynitrite\": 23451,\n \"amazon\": 23452,\n \"nbs\": 23453,\n \"hydroxyethyl\": 23454,\n \"hemophilia\": 23455,\n \"myopic\": 23456,\n \"microvessels\": 23457,\n \"street\": 23458,\n \"initiator\": 23459,\n \"obligate\": 23460,\n \"television\": 23461,\n \"##idosis\": 23462,\n \"counsel\": 23463,\n \"##oide\": 23464,\n \"##axanthin\": 23465,\n \"##eme\": 23466,\n \"overestimated\": 23467,\n \"nord\": 23468,\n \"249\": 23469,\n \"##ectivities\": 23470,\n \"thyrotropin\": 23471,\n \"recovering\": 23472,\n \"qtls\": 23473,\n \"earthqu\": 23474,\n \"internalizing\": 23475,\n \"##onus\": 23476,\n \"##liptin\": 23477,\n \"bottleneck\": 23478,\n \"trapez\": 23479,\n \"mvc\": 23480,\n \"040\": 23481,\n \"cld\": 23482,\n \"plasmacyt\": 23483,\n \"2500\": 23484,\n \"tung\": 23485,\n \"diamin\": 23486,\n \"evoke\": 23487,\n \"nucleolar\": 23488,\n \"fundamentally\": 23489,\n \"repeating\": 23490,\n \"transformants\": 23491,\n \"caribbean\": 23492,\n \"##wl\": 23493,\n \"wastes\": 23494,\n \"##equencing\": 23495,\n \"arthro\": 23496,\n \"cd38\": 23497,\n \"dystrophic\": 23498,\n \"vg\": 23499,\n \"invariably\": 23500,\n \"caveolin\": 23501,\n \"##itizing\": 23502,\n \"charcoal\": 23503,\n \"unravel\": 23504,\n \"delaying\": 23505,\n \"sock\": 23506,\n \"244\": 23507,\n \"formats\": 23508,\n \"pyrolysis\": 23509,\n \"emin\": 23510,\n \"psii\": 23511,\n \"bpy\": 23512,\n \"cpm\": 23513,\n \"hek293\": 23514,\n \"captive\": 23515,\n \"shells\": 23516,\n \"schoolchildren\": 23517,\n \"cardiometabolic\": 23518,\n \"##7a\": 23519,\n \"tars\": 23520,\n \"empowerment\": 23521,\n \"##agenic\": 23522,\n \"extrapolation\": 23523,\n \"itrac\": 23524,\n \"amik\": 23525,\n \"equity\": 23526,\n \"295\": 23527,\n \"query\": 23528,\n \"eicos\": 23529,\n \"tetrazolium\": 23530,\n \"##qs\": 23531,\n \"##⁻¹\": 23532,\n \"##coming\": 23533,\n \"##body\": 23534,\n \"canopy\": 23535,\n \"##har\": 23536,\n \"tympanic\": 23537,\n \"insults\": 23538,\n \"diesel\": 23539,\n \"pteryg\": 23540,\n \"vertically\": 23541,\n \"intradermal\": 23542,\n \"appreci\": 23543,\n \"spheroids\": 23544,\n \"ddp\": 23545,\n \"##cephaly\": 23546,\n \"18s\": 23547,\n \"duplications\": 23548,\n \"preoptic\": 23549,\n \"##arbox\": 23550,\n \"247\": 23551,\n \"kupffer\": 23552,\n \"mercapt\": 23553,\n \"##enclamide\": 23554,\n \"irin\": 23555,\n \"hch\": 23556,\n \"##onsin\": 23557,\n \"cpe\": 23558,\n \"recirc\": 23559,\n \"tata\": 23560,\n \"emboli\": 23561,\n \"protozoan\": 23562,\n \"heterotopic\": 23563,\n \"certainty\": 23564,\n \"##epsia\": 23565,\n \"253\": 23566,\n \"##anate\": 23567,\n \"pretest\": 23568,\n \"##utres\": 23569,\n \"thermophilic\": 23570,\n \"440\": 23571,\n \"abstraction\": 23572,\n \"telomeres\": 23573,\n \"vegfr\": 23574,\n \"##onders\": 23575,\n \"adenosyl\": 23576,\n \"intragastric\": 23577,\n \"cycloaddition\": 23578,\n \"spm\": 23579,\n \"##orescence\": 23580,\n \"mercapto\": 23581,\n \"alumina\": 23582,\n \"tentatively\": 23583,\n \"advocacy\": 23584,\n \"gk\": 23585,\n \"pnd\": 23586,\n \"##vhd\": 23587,\n \"stays\": 23588,\n \"temperament\": 23589,\n \"compress\": 23590,\n \"##amili\": 23591,\n \"bullying\": 23592,\n \"unfavour\": 23593,\n \"habituation\": 23594,\n \"##retinal\": 23595,\n \"emr\": 23596,\n \"##ocholine\": 23597,\n \"substitutes\": 23598,\n \"retail\": 23599,\n \"confidential\": 23600,\n \"##iso\": 23601,\n \"paraventricular\": 23602,\n \"scopol\": 23603,\n \"wal\": 23604,\n \"conspecific\": 23605,\n \"completeness\": 23606,\n \"antihist\": 23607,\n \"characterise\": 23608,\n \"catastrophic\": 23609,\n \"excip\": 23610,\n \"deoxyribonucle\": 23611,\n \"ceftazid\": 23612,\n \"glib\": 23613,\n \"orbitals\": 23614,\n \"lipof\": 23615,\n \"##atur\": 23616,\n \"transcutaneous\": 23617,\n \"##che\": 23618,\n \"precautions\": 23619,\n \"kinin\": 23620,\n \"minimization\": 23621,\n \"formic\": 23622,\n \"dyspepsia\": 23623,\n \"##eptor\": 23624,\n \"itraconazole\": 23625,\n \"ramp\": 23626,\n \"bisphosphate\": 23627,\n \"parag\": 23628,\n \"phenols\": 23629,\n \"peru\": 23630,\n \"odontogenic\": 23631,\n \"profit\": 23632,\n \"fluorodeoxy\": 23633,\n \"##worth\": 23634,\n \"tpn\": 23635,\n \"reversibility\": 23636,\n \"hil\": 23637,\n \"extras\": 23638,\n \"infiltrated\": 23639,\n \"##tring\": 23640,\n \"nes\": 23641,\n \"senescent\": 23642,\n \"autogenous\": 23643,\n \"erps\": 23644,\n \"naltrexone\": 23645,\n \"##344\": 23646,\n \"##fi\": 23647,\n \"capd\": 23648,\n \"radioligand\": 23649,\n \"contours\": 23650,\n \"affairs\": 23651,\n \"altman\": 23652,\n \"##enolol\": 23653,\n \"dehiscence\": 23654,\n \"synovi\": 23655,\n \"loud\": 23656,\n \"rs10\": 23657,\n \"assumes\": 23658,\n \"fms\": 23659,\n \"amikacin\": 23660,\n \"irinotecan\": 23661,\n \"smcs\": 23662,\n \"cephalosporins\": 23663,\n \"conferring\": 23664,\n \"insula\": 23665,\n \"mismatched\": 23666,\n \"oxidoreductase\": 23667,\n \"concert\": 23668,\n \"##ysteroid\": 23669,\n \"restricting\": 23670,\n \"redundancy\": 23671,\n \"##ionyl\": 23672,\n \"sorbitol\": 23673,\n \"dislocations\": 23674,\n \"##ryl\": 23675,\n \"##lers\": 23676,\n \"cereal\": 23677,\n \"##lingual\": 23678,\n \"bart\": 23679,\n \"##roke\": 23680,\n \"igt\": 23681,\n \"queens\": 23682,\n \"nipple\": 23683,\n \"buffering\": 23684,\n \"scalable\": 23685,\n \"##oneu\": 23686,\n \"permanently\": 23687,\n \"chaotic\": 23688,\n \"ganglioside\": 23689,\n \"##iaxial\": 23690,\n \"oxygenated\": 23691,\n \"compositional\": 23692,\n \"inclusive\": 23693,\n \"hypothyroid\": 23694,\n \"internationally\": 23695,\n \"farnes\": 23696,\n \"thinner\": 23697,\n \"gulf\": 23698,\n \"crystallinity\": 23699,\n \"##hb\": 23700,\n \"placentas\": 23701,\n \"cartrid\": 23702,\n \"wisconsin\": 23703,\n \"##famide\": 23704,\n \"##oprophylaxis\": 23705,\n \"wheelch\": 23706,\n \"recalled\": 23707,\n \"alkylating\": 23708,\n \"activations\": 23709,\n \"electroporation\": 23710,\n \"bival\": 23711,\n \"cob\": 23712,\n \"bras\": 23713,\n \"lrr\": 23714,\n \"convincing\": 23715,\n \"ld50\": 23716,\n \"antifer\": 23717,\n \"intensively\": 23718,\n \"dpi\": 23719,\n \"##nn\": 23720,\n \"athlete\": 23721,\n \"##obutyl\": 23722,\n \"cystitis\": 23723,\n \"haemorrhagic\": 23724,\n \"hilar\": 23725,\n \"##reach\": 23726,\n \"ceftazidime\": 23727,\n \"splanch\": 23728,\n \"exhaustive\": 23729,\n \"1971\": 23730,\n \"myelopathy\": 23731,\n \"vitil\": 23732,\n \"##tiform\": 23733,\n \"##atri\": 23734,\n \"massage\": 23735,\n \"##a3\": 23736,\n \"administrators\": 23737,\n \"cpc\": 23738,\n \"##rey\": 23739,\n \"polyureth\": 23740,\n \"autophagic\": 23741,\n \"pgr\": 23742,\n \"divide\": 23743,\n \"glue\": 23744,\n \"rts\": 23745,\n \"vta\": 23746,\n \"deposit\": 23747,\n \"##ibrils\": 23748,\n \"fork\": 23749,\n \"##ll\": 23750,\n \"##ycholic\": 23751,\n \"defibrillator\": 23752,\n \"orfs\": 23753,\n \"threatened\": 23754,\n \"leukemias\": 23755,\n \"appointment\": 23756,\n \"ira\": 23757,\n \"merist\": 23758,\n \"isc\": 23759,\n \"cape\": 23760,\n \"myr\": 23761,\n \"cytostatic\": 23762,\n \"videos\": 23763,\n \"agen\": 23764,\n \"cohen\": 23765,\n \"northeast\": 23766,\n \"dash\": 23767,\n \"informatics\": 23768,\n \"malaysia\": 23769,\n \"governance\": 23770,\n \"kainate\": 23771,\n \"adnex\": 23772,\n \"bears\": 23773,\n \"dihydroxyvitamin\": 23774,\n \"capping\": 23775,\n \"replicative\": 23776,\n \"uracil\": 23777,\n \"bcva\": 23778,\n \"villi\": 23779,\n \"ats\": 23780,\n \"cyclopent\": 23781,\n \"rsa\": 23782,\n \"proposals\": 23783,\n \"goblet\": 23784,\n \"papilla\": 23785,\n \"##amole\": 23786,\n \"bulls\": 23787,\n \"##oethylene\": 23788,\n \"castrated\": 23789,\n \"illumina\": 23790,\n \"oint\": 23791,\n \"oesophagus\": 23792,\n \"##athin\": 23793,\n \"spouses\": 23794,\n \"trains\": 23795,\n \"pdac\": 23796,\n \"spider\": 23797,\n \"lowers\": 23798,\n \"ownership\": 23799,\n \"##l3\": 23800,\n \"decont\": 23801,\n \"craniotomy\": 23802,\n \"disproportionately\": 23803,\n \"2p\": 23804,\n \"mph\": 23805,\n \"crowding\": 23806,\n \"##osarcomas\": 23807,\n \"##cedural\": 23808,\n \"pvc\": 23809,\n \"chelation\": 23810,\n \"##itism\": 23811,\n \"invertebrate\": 23812,\n \"##fm\": 23813,\n \"ipt\": 23814,\n \"transferring\": 23815,\n \"pge1\": 23816,\n \"scfv\": 23817,\n \"olt\": 23818,\n \"proteinases\": 23819,\n \"##ums\": 23820,\n \"hscr\": 23821,\n \"pae\": 23822,\n \"isotype\": 23823,\n \"favors\": 23824,\n \"rantes\": 23825,\n \"268\": 23826,\n \"decidual\": 23827,\n \"unfamili\": 23828,\n \"immunology\": 23829,\n \"atopy\": 23830,\n \"typed\": 23831,\n \"##econds\": 23832,\n \"tie\": 23833,\n \"lift\": 23834,\n \"glut4\": 23835,\n \"smr\": 23836,\n \"ille\": 23837,\n \"diphosph\": 23838,\n \"##olamine\": 23839,\n \"##angiect\": 23840,\n \"humid\": 23841,\n \"hyperinsulinemia\": 23842,\n \"biochar\": 23843,\n \"cervic\": 23844,\n \"##factorily\": 23845,\n \"##romal\": 23846,\n \"atenolol\": 23847,\n \"attrition\": 23848,\n \"##aciens\": 23849,\n \"converts\": 23850,\n \"faith\": 23851,\n \"morris\": 23852,\n \"vaccinia\": 23853,\n \"ethoxy\": 23854,\n \"nq\": 23855,\n \"##junction\": 23856,\n \"moves\": 23857,\n \"dispersions\": 23858,\n \"fertilized\": 23859,\n \"periventricular\": 23860,\n \"parat\": 23861,\n \"reca\": 23862,\n \"nonsurgical\": 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\"prolonging\": 26391,\n \"##olecule\": 26392,\n \"dialogue\": 26393,\n \"corpora\": 26394,\n \"cartilaginous\": 26395,\n \"gct\": 26396,\n \"submerged\": 26397,\n \"isogenic\": 26398,\n \"proliferated\": 26399,\n \"carinii\": 26400,\n \"erbb2\": 26401,\n \"calcitriol\": 26402,\n \"stimulants\": 26403,\n \"##worms\": 26404,\n \"pollination\": 26405,\n \"tribut\": 26406,\n \"##odial\": 26407,\n \"purpos\": 26408,\n \"##crystalline\": 26409,\n \"obvi\": 26410,\n \"inte\": 26411,\n \"expansions\": 26412,\n \"angiogram\": 26413,\n \"euthanasia\": 26414,\n \"##ontally\": 26415,\n \"duch\": 26416,\n \"gloss\": 26417,\n \"##onolactone\": 26418,\n \"tagging\": 26419,\n \"paranasal\": 26420,\n \"reminiscent\": 26421,\n \"keratoconus\": 26422,\n \"antegr\": 26423,\n \"alphab\": 26424,\n \"hydroxylated\": 26425,\n \"##yxin\": 26426,\n \"concentrating\": 26427,\n \"spans\": 26428,\n \"accuracies\": 26429,\n \"pemphigus\": 26430,\n \"wss\": 26431,\n \"raw264\": 26432,\n \"##ynchronization\": 26433,\n \"##rimers\": 26434,\n \"hete\": 26435,\n \"##onto\": 26436,\n \"nph\": 26437,\n \"323\": 26438,\n \"adipogenesis\": 26439,\n \"##co3\": 26440,\n \"arginase\": 26441,\n \"triam\": 26442,\n \"##jury\": 26443,\n \"appliance\": 26444,\n \"##hole\": 26445,\n \"dressings\": 26446,\n \"##ictal\": 26447,\n \"colloids\": 26448,\n \"advisory\": 26449,\n \"avf\": 26450,\n \"microsphere\": 26451,\n \"##uities\": 26452,\n \"##ofrontal\": 26453,\n \"daltons\": 26454,\n \"##urative\": 26455,\n \"560\": 26456,\n \"underscores\": 26457,\n \"hone\": 26458,\n \"##athesis\": 26459,\n \"rhd\": 26460,\n \"##isations\": 26461,\n \"lil\": 26462,\n \"transglut\": 26463,\n \"photocoagulation\": 26464,\n \"breadth\": 26465,\n \"enantiomeric\": 26466,\n \"ablated\": 26467,\n \"methox\": 26468,\n \"supraventricular\": 26469,\n \"decontamination\": 26470,\n \"##asy\": 26471,\n \"reticulocyte\": 26472,\n \"h5n1\": 26473,\n \"dps\": 26474,\n \"substantiated\": 26475,\n \"tukey\": 26476,\n \"biochem\": 26477,\n \"enclosed\": 26478,\n \"esp\": 26479,\n \"apheresis\": 26480,\n \"satisfying\": 26481,\n \"vk\": 26482,\n \"infinity\": 26483,\n \"d5\": 26484,\n \"sulphur\": 26485,\n \"sympos\": 26486,\n \"neointimal\": 26487,\n \"chromatid\": 26488,\n \"thymocyte\": 26489,\n \"tapping\": 26490,\n \"repulsion\": 26491,\n \"muco\": 26492,\n \"invaluable\": 26493,\n \"delphi\": 26494,\n \"absolutely\": 26495,\n \"silicate\": 26496,\n \"355\": 26497,\n \"diminution\": 26498,\n \"ergonomic\": 26499,\n \"afternoon\": 26500,\n \"smartphone\": 26501,\n \"ropivacaine\": 26502,\n \"tdt\": 26503,\n \"valsalva\": 26504,\n \"pests\": 26505,\n \"##uries\": 26506,\n \"dehp\": 26507,\n \"##ophosphorylation\": 26508,\n \"fullerene\": 26509,\n \"normoxia\": 26510,\n \"lewy\": 26511,\n \"cucumber\": 26512,\n \"aec\": 26513,\n \"refolding\": 26514,\n \"recalcitr\": 26515,\n \"##urd\": 26516,\n \"minorities\": 26517,\n \"premalign\": 26518,\n \"favoured\": 26519,\n \"osseoin\": 26520,\n \"architectural\": 26521,\n \"microangi\": 26522,\n \"brushing\": 26523,\n \"gastrost\": 26524,\n \"mayo\": 26525,\n \"bsi\": 26526,\n \"homeobox\": 26527,\n \"vinblastine\": 26528,\n \"##ophile\": 26529,\n \"irreversibly\": 26530,\n \"##tised\": 26531,\n \"##iasm\": 26532,\n \"neuroradi\": 26533,\n \"gust\": 26534,\n \"accountability\": 26535,\n \"p70\": 26536,\n \"vp1\": 26537,\n \"##zolid\": 26538,\n \"spouse\": 26539,\n \"founder\": 26540,\n \"tec\": 26541,\n \"multiply\": 26542,\n \"rape\": 26543,\n \"rosette\": 26544,\n \"gstm1\": 26545,\n \"provincial\": 26546,\n \"syd\": 26547,\n \"parane\": 26548,\n \"pyogenes\": 26549,\n \"cd86\": 26550,\n \"mumps\": 26551,\n \"mig\": 26552,\n \"guanidine\": 26553,\n \"lentivirus\": 26554,\n \"perfringens\": 26555,\n \"abusers\": 26556,\n \"435\": 26557,\n \"ppis\": 26558,\n \"##holm\": 26559,\n \"##ached\": 26560,\n \"eac\": 26561,\n \"retrotransp\": 26562,\n \"unequal\": 26563,\n \"gelation\": 26564,\n \"##odality\": 26565,\n \"arbor\": 26566,\n \"motives\": 26567,\n \"neurofilament\": 26568,\n \"##idov\": 26569,\n \"skf\": 26570,\n \"pluripotency\": 26571,\n \"serca\": 26572,\n \"grasp\": 26573,\n \"##ughters\": 26574,\n \"derivatized\": 26575,\n \"spermatids\": 26576,\n \"##±6\": 26577,\n \"thaps\": 26578,\n \"dmp\": 26579,\n \"https\": 26580,\n \"fors\": 26581,\n \"hrql\": 26582,\n \"invag\": 26583,\n \"collagenous\": 26584,\n \"##refring\": 26585,\n \"##icon\": 26586,\n \"vocational\": 26587,\n \"reluct\": 26588,\n \"3r\": 26589,\n \"articulation\": 26590,\n \"dimensionality\": 26591,\n \"gallium\": 26592,\n \"solubil\": 26593,\n \"rhinosinus\": 26594,\n \"rotary\": 26595,\n \"##t4\": 26596,\n \"naphthyl\": 26597,\n \"##spr\": 26598,\n \"##tens\": 26599,\n \"dihydroxyphenyl\": 26600,\n \"assisting\": 26601,\n \"bont\": 26602,\n \"chondrogenic\": 26603,\n \"##util\": 26604,\n \"ceramics\": 26605,\n \"unfractionated\": 26606,\n \"governmental\": 26607,\n \"birefring\": 26608,\n \"307\": 26609,\n \"meteorological\": 26610,\n \"trx\": 26611,\n \"coarct\": 26612,\n \"tadp\": 26613,\n \"orofacial\": 26614,\n \"##phalan\": 26615,\n \"1969\": 26616,\n \"anterolateral\": 26617,\n \"immigration\": 26618,\n \"glassy\": 26619,\n \"ers\": 26620,\n \"tunis\": 26621,\n \"precipitates\": 26622,\n \"propylene\": 26623,\n \"324\": 26624,\n \"removes\": 26625,\n \"##olimus\": 26626,\n \"dtc\": 26627,\n \"multiform\": 26628,\n \"##acetam\": 26629,\n \"avenue\": 26630,\n \"reint\": 26631,\n \"##otetr\": 26632,\n \"tonsillectomy\": 26633,\n \"##atalysts\": 26634,\n \"##iscal\": 26635,\n \"##bm\": 26636,\n \"impairing\": 26637,\n \"carcinogenicity\": 26638,\n \"##organized\": 26639,\n \"preponder\": 26640,\n \"convolution\": 26641,\n \"variceal\": 26642,\n \"workshops\": 26643,\n \"earthquake\": 26644,\n \"sunflower\": 26645,\n \"updating\": 26646,\n \"unity\": 26647,\n \"cued\": 26648,\n \"prematurely\": 26649,\n \"epigastric\": 26650,\n \"effected\": 26651,\n \"pbdes\": 26652,\n \"bags\": 26653,\n \"anticipatory\": 26654,\n \"dyspnoea\": 26655,\n \"deriving\": 26656,\n \"leachate\": 26657,\n \"postures\": 26658,\n \"mfs\": 26659,\n \"chelate\": 26660,\n \"pyloric\": 26661,\n \"crustace\": 26662,\n \"##ibi\": 26663,\n \"antegrade\": 26664,\n \"##carbonyl\": 26665,\n \"diffusive\": 26666,\n \"finished\": 26667,\n \"neutrophilic\": 26668,\n \"diminishes\": 26669,\n \"tanzania\": 26670,\n \"##utamide\": 26671,\n \"incurred\": 26672,\n \"sunitinib\": 26673,\n \"metabotropic\": 26674,\n \"##plasmic\": 26675,\n \"precoci\": 26676,\n \"cd95\": 26677,\n \"##hss\": 26678,\n \"p0\": 26679,\n \"##ilol\": 26680,\n \"##udinal\": 26681,\n \"proving\": 26682,\n \"ddd\": 26683,\n \"cdr\": 26684,\n \"simplify\": 26685,\n \"phenotyping\": 26686,\n \"operant\": 26687,\n \"pyreth\": 26688,\n \"gefitinib\": 26689,\n \"labyrinth\": 26690,\n \"ivermectin\": 26691,\n \"oleate\": 26692,\n \"cornerstone\": 26693,\n \"irb\": 26694,\n \"314\": 26695,\n \"reinst\": 26696,\n \"hoped\": 26697,\n \"iad\": 26698,\n \"staurosporine\": 26699,\n \"##glycine\": 26700,\n \"aptamers\": 26701,\n \"melphalan\": 26702,\n \"t6\": 26703,\n \"insulator\": 26704,\n \"##oub\": 26705,\n \"byproduc\": 26706,\n \"oviduct\": 26707,\n \"ptr\": 26708,\n \"##nut\": 26709,\n \"sirs\": 26710,\n \"aat\": 26711,\n \"##onitor\": 26712,\n \"pvl\": 26713,\n \"##tilbene\": 26714,\n \"expired\": 26715,\n \"amides\": 26716,\n \"##rocar\": 26717,\n \"##ophilicity\": 26718,\n \"weap\": 26719,\n \"isokinetic\": 26720,\n \"##ifn\": 26721,\n \"nonobese\": 26722,\n \"paradoxically\": 26723,\n \"rhinosinusitis\": 26724,\n \"ted\": 26725,\n \"dls\": 26726,\n \"proteas\": 26727,\n \"##ilson\": 26728,\n \"suicides\": 26729,\n \"galnac\": 26730,\n \"398\": 26731,\n \"dermatological\": 26732,\n \"landfill\": 26733,\n \"septicemia\": 26734,\n \"citation\": 26735,\n \"relaxations\": 26736,\n \"conceal\": 26737,\n \"thereof\": 26738,\n \"disclose\": 26739,\n \"psg\": 26740,\n \"johnson\": 26741,\n \"nonionic\": 26742,\n \"confronted\": 26743,\n \"1960s\": 26744,\n \"##oserine\": 26745,\n \"##pyruvate\": 26746,\n \"carboxyp\": 26747,\n \"thymoma\": 26748,\n \"immunopos\": 26749,\n \"##bling\": 26750,\n \"328\": 26751,\n \"##me3\": 26752,\n \"nylon\": 26753,\n \"##ventional\": 26754,\n \"rpa\": 26755,\n \"##4b\": 26756,\n \"tbp\": 26757,\n \"critic\": 26758,\n \"pancreatectomy\": 26759,\n \"voltages\": 26760,\n \"##akic\": 26761,\n \"ipd\": 26762,\n \"mtr\": 26763,\n \"338\": 26764,\n \"calendar\": 26765,\n \"nestin\": 26766,\n \"##aci\": 26767,\n \"overuse\": 26768,\n \"mycorrhizal\": 26769,\n \"premise\": 26770,\n \"cacl2\": 26771,\n \"firmly\": 26772,\n \"##ilation\": 26773,\n \"wrink\": 26774,\n \"quinidine\": 26775,\n \"anthel\": 26776,\n \"17β\": 26777,\n \"veterin\": 26778,\n \"nucleoli\": 26779,\n \"radiosensitivity\": 26780,\n \"cuts\": 26781,\n \"lk\": 26782,\n \"endangered\": 26783,\n \"hybridisation\": 26784,\n \"attribution\": 26785,\n \"##ogas\": 26786,\n \"exhaust\": 26787,\n \"##vular\": 26788,\n \"##fb\": 26789,\n \"dihydrotestosterone\": 26790,\n \"dicty\": 26791,\n \"typhi\": 26792,\n \"perit\": 26793,\n \"##occus\": 26794,\n \"dock\": 26795,\n \"##peri\": 26796,\n \"##opard\": 26797,\n \"pyros\": 26798,\n \"ptt\": 26799,\n \"mict\": 26800,\n \"divor\": 26801,\n \"spme\": 26802,\n \"solit\": 26803,\n \"##neumonia\": 26804,\n \"sponges\": 26805,\n \"jam\": 26806,\n \"lir\": 26807,\n \"mendelian\": 26808,\n \"daughters\": 26809,\n \"snf\": 26810,\n \"transr\": 26811,\n \"capec\": 26812,\n \"guanylate\": 26813,\n \"##ptics\": 26814,\n \"polymerized\": 26815,\n \"decompensated\": 26816,\n \"enters\": 26817,\n \"linezolid\": 26818,\n \"##obal\": 26819,\n \"##pps\": 26820,\n \"hemolymph\": 26821,\n \"cardioverter\": 26822,\n \"interviewing\": 26823,\n \"caa\": 26824,\n \"salping\": 26825,\n \"##oliation\": 26826,\n \"##mscs\": 26827,\n \"4e\": 26828,\n \"bioaccumulation\": 26829,\n \"##ohydrolase\": 26830,\n \"##ecture\": 26831,\n \"cyanobacterial\": 26832,\n \"sternotomy\": 26833,\n \"##imed\": 26834,\n \"download\": 26835,\n \"cannabinoids\": 26836,\n \"##vp\": 26837,\n \"assistant\": 26838,\n \"##l4\": 26839,\n \"patern\": 26840,\n \"etch\": 26841,\n \"populated\": 26842,\n \"bse\": 26843,\n \"micronuclei\": 26844,\n \"cytokinesis\": 26845,\n \"subchondral\": 26846,\n \"spinach\": 26847,\n \"##omus\": 26848,\n \"lmw\": 26849,\n \"spotted\": 26850,\n \"##bin\": 26851,\n \"clinico\": 26852,\n \"hypothesised\": 26853,\n \"nulliparous\": 26854,\n \"butyric\": 26855,\n \"lobar\": 26856,\n \"##obacillus\": 26857,\n \"paroxetine\": 26858,\n \"oryzae\": 26859,\n \"cleave\": 26860,\n \"neomycin\": 26861,\n \"rutin\": 26862,\n \"phenolics\": 26863,\n \"egypt\": 26864,\n \"##fd\": 26865,\n \"336\": 26866,\n \"322\": 26867,\n \"##ophiles\": 26868,\n \"pedestr\": 26869,\n \"##ospores\": 26870,\n \"aroma\": 26871,\n \"##amptic\": 26872,\n \"grief\": 26873,\n \"##3c\": 26874,\n \"##izyg\": 26875,\n \"##kel\": 26876,\n \"subfraction\": 26877,\n \"colombia\": 26878,\n \"pann\": 26879,\n \"ctcs\": 26880,\n \"noninvasively\": 26881,\n \"##oda\": 26882,\n \"hydroxysteroid\": 26883,\n \"macula\": 26884,\n \"##anthus\": 26885,\n \"pht\": 26886,\n \"326\": 26887,\n \"kyphosis\": 26888,\n \"phytohemagglutinin\": 26889,\n \"calb\": 26890,\n \"sangu\": 26891,\n \"##alact\": 26892,\n \"assistants\": 26893,\n \"photothermal\": 26894,\n \"ffm\": 26895,\n \"##ocally\": 26896,\n \"malle\": 26897,\n \"subsid\": 26898,\n \"capecitabine\": 26899,\n \"trkb\": 26900,\n \"eradicate\": 26901,\n \"victoria\": 26902,\n \"proapoptotic\": 26903,\n \"laminae\": 26904,\n \"bsp\": 26905,\n \"##road\": 26906,\n \"##ometrics\": 26907,\n \"12p\": 26908,\n \"handicap\": 26909,\n \"entom\": 26910,\n \"##4002\": 26911,\n \"dhe\": 26912,\n \"##adenoma\": 26913,\n \"abstracted\": 26914,\n \"##pervised\": 26915,\n \"equatorial\": 26916,\n \"pvs\": 26917,\n \"fluorescently\": 26918,\n \"worsen\": 26919,\n \"percentiles\": 26920,\n \"##aresis\": 26921,\n \"circumscribed\": 26922,\n \"continental\": 26923,\n \"empy\": 26924,\n \"mussels\": 26925,\n \"thapsig\": 26926,\n \"##yi\": 26927,\n \"##occup\": 26928,\n \"tritic\": 26929,\n \"tcf\": 26930,\n \"##uating\": 26931,\n \"317\": 26932,\n \"improper\": 26933,\n \"anecd\": 26934,\n \"bursting\": 26935,\n \"apatite\": 26936,\n \"globus\": 26937,\n \"skelet\": 26938,\n \"labial\": 26939,\n \"##atement\": 26940,\n \"squirrel\": 26941,\n \"broc\": 26942,\n \"photop\": 26943,\n \"sponsored\": 26944,\n \"prk\": 26945,\n \"nonlinearity\": 26946,\n \"adalimumab\": 26947,\n \"cetuximab\": 26948,\n \"subtr\": 26949,\n \"neurotensin\": 26950,\n \"metallo\": 26951,\n \"fumarate\": 26952,\n \"quorum\": 26953,\n \"mitogens\": 26954,\n \"barely\": 26955,\n \"conformal\": 26956,\n \"grant\": 26957,\n \"mesothelial\": 26958,\n \"##din\": 26959,\n \"6b\": 26960,\n \"##imbic\": 26961,\n \"bonfer\": 26962,\n \"marc\": 26963,\n \"misinterpre\": 26964,\n \"##atentorial\": 26965,\n \"numb\": 26966,\n \"diox\": 26967,\n \"##urational\": 26968,\n \"intercept\": 26969,\n \"panor\": 26970,\n \"paraplegia\": 26971,\n \"variances\": 26972,\n \"looks\": 26973,\n \"renewable\": 26974,\n \"pgd\": 26975,\n \"##160\": 26976,\n \"##ohepatitis\": 26977,\n \"##adias\": 26978,\n \"volv\": 26979,\n \"microelectrode\": 26980,\n \"##2p\": 26981,\n \"reven\": 26982,\n \"##ocerc\": 26983,\n \"forehead\": 26984,\n \"superconducting\": 26985,\n \"cd40l\": 26986,\n \"coryneb\": 26987,\n \"apoc\": 26988,\n \"cynomol\": 26989,\n \"counteracted\": 26990,\n \"dealt\": 26991,\n \"widening\": 26992,\n \"##vr\": 26993,\n \"neuropathies\": 26994,\n \"stan\": 26995,\n \"##alp\": 26996,\n \"ida\": 26997,\n \"spiro\": 26998,\n \"thuring\": 26999,\n \"exotic\": 27000,\n \"commenced\": 27001,\n \"spared\": 27002,\n \"##hyper\": 27003,\n \"telec\": 27004,\n \"##ocystein\": 27005,\n \"everolimus\": 27006,\n \"thapsigargin\": 27007,\n \"tendin\": 27008,\n \"abolish\": 27009,\n \"rhabdomyosarcoma\": 27010,\n \"##abric\": 27011,\n \"fivefold\": 27012,\n \"phobia\": 27013,\n \"benzoic\": 27014,\n \"nociception\": 27015,\n \"##opexy\": 27016,\n \"degenerated\": 27017,\n \"lta\": 27018,\n \"ym\": 27019,\n \"indolent\": 27020,\n \"hunger\": 27021,\n \"quantitate\": 27022,\n \"1968\": 27023,\n \"amitriptyline\": 27024,\n \"inval\": 27025,\n \"nonsyn\": 27026,\n \"psychologists\": 27027,\n \"endosperm\": 27028,\n \"humanized\": 27029,\n \"ascs\": 27030,\n \"##cl3\": 27031,\n \"meanings\": 27032,\n \"hairs\": 27033,\n \"pws\": 27034,\n \"hypotonic\": 27035,\n \"jn\": 27036,\n \"##omycetes\": 27037,\n \"skewed\": 27038,\n \"orthodon\": 27039,\n \"hamm\": 27040,\n \"##oprofen\": 27041,\n \"osteopenia\": 27042,\n \"aplastic\": 27043,\n \"exome\": 27044,\n \"leukopenia\": 27045,\n \"zidov\": 27046,\n \"neurotrophin\": 27047,\n \"nicotiana\": 27048,\n \"transrectal\": 27049,\n \"pneumocystis\": 27050,\n \"e2f\": 27051,\n \"interferences\": 27052,\n \"##eedback\": 27053,\n \"handled\": 27054,\n \"##ipes\": 27055,\n \"resonator\": 27056,\n \"multipl\": 27057,\n \"lc50\": 27058,\n \"##fed\": 27059,\n \"coadministration\": 27060,\n \"anhydro\": 27061,\n \"291\": 27062,\n \"walker\": 27063,\n \"##peak\": 27064,\n \"auricular\": 27065,\n \"inoperable\": 27066,\n \"mainland\": 27067,\n \"oriental\": 27068,\n \"##umer\": 27069,\n \"prein\": 27070,\n \"##ymb\": 27071,\n \"ccp\": 27072,\n \"therapeutical\": 27073,\n \"##enzymatic\": 27074,\n \"localities\": 27075,\n \"hydroxybenz\": 27076,\n \"draws\": 27077,\n \"lysed\": 27078,\n \"ecologically\": 27079,\n \"myriad\": 27080,\n \"##elen\": 27081,\n \"mt1\": 27082,\n \"decoding\": 27083,\n \"uncharacterized\": 27084,\n \"##ymet\": 27085,\n \"hypere\": 27086,\n \"##otypical\": 27087,\n \"hemin\": 27088,\n \"hydroxyurea\": 27089,\n \"##ucher\": 27090,\n \"berberine\": 27091,\n \"eud\": 27092,\n \"##piper\": 27093,\n \"examiners\": 27094,\n \"peculiarities\": 27095,\n \"microti\": 27096,\n \"##estrus\": 27097,\n \"##odeoxycholic\": 27098,\n \"pepper\": 27099,\n \"##asters\": 27100,\n \"hash\": 27101,\n \"autocor\": 27102,\n \"repell\": 27103,\n \"formalism\": 27104,\n \"sids\": 27105,\n \"denoted\": 27106,\n \"##cyt\": 27107,\n \"premalignant\": 27108,\n \"ims\": 27109,\n \"blade\": 27110,\n \"scavengers\": 27111,\n \"stx\": 27112,\n \"meant\": 27113,\n \"zidovudine\": 27114,\n \"readout\": 27115,\n \"mwcnts\": 27116,\n \"lactobacilli\": 27117,\n \"knew\": 27118,\n \"tetradecanoyl\": 27119,\n \"mated\": 27120,\n \"trimer\": 27121,\n \"dde\": 27122,\n \"ucl\": 27123,\n \"p5\": 27124,\n \"biomolecular\": 27125,\n \"wilson\": 27126,\n \"opposition\": 27127,\n \"dendrimers\": 27128,\n \"entries\": 27129,\n \"1965\": 27130,\n \"electronically\": 27131,\n \"##iliac\": 27132,\n \"opaque\": 27133,\n \"polyelectrolyte\": 27134,\n \"##v2\": 27135,\n \"##ims\": 27136,\n \"##openem\": 27137,\n \"pex\": 27138,\n \"organisational\": 27139,\n \"itch\": 27140,\n \"dipeptide\": 27141,\n \"abutment\": 27142,\n \"varicose\": 27143,\n \"asi\": 27144,\n \"dissect\": 27145,\n \"preserves\": 27146,\n \"intercalated\": 27147,\n \"relaxant\": 27148,\n \"advisable\": 27149,\n \"gastroduoden\": 27150,\n \"serologically\": 27151,\n \"enhancements\": 27152,\n \"342\": 27153,\n \"thermograv\": 27154,\n \"texts\": 27155,\n \"corroborate\": 27156,\n \"##odular\": 27157,\n \"agonistic\": 27158,\n \"policym\": 27159,\n \"reuse\": 27160,\n \"coincides\": 27161,\n \"provisional\": 27162,\n \"enterpr\": 27163,\n \"stepping\": 27164,\n \"deflection\": 27165,\n \"verruc\": 27166,\n \"normalize\": 27167,\n \"cardiov\": 27168,\n \"entrain\": 27169,\n \"polio\": 27170,\n \"cyp2c19\": 27171,\n \"##oretinal\": 27172,\n \"##rolases\": 27173,\n \"##mates\": 27174,\n \"##usen\": 27175,\n \"really\": 27176,\n \"harsh\": 27177,\n \"duchenne\": 27178,\n \"cd18\": 27179,\n \"ebola\": 27180,\n \"gelatinase\": 27181,\n \"##apentaenoic\": 27182,\n \"antitum\": 27183,\n \"relaxing\": 27184,\n \"chx\": 27185,\n \"fallopian\": 27186,\n \"##opropane\": 27187,\n \"advertising\": 27188,\n \"entails\": 27189,\n \"packaged\": 27190,\n \"##c12\": 27191,\n \"pud\": 27192,\n \"clipping\": 27193,\n \"command\": 27194,\n \"officinal\": 27195,\n \"ft4\": 27196,\n \"frameshift\": 27197,\n \"tour\": 27198,\n \"sh2\": 27199,\n \"centuries\": 27200,\n \"##uta\": 27201,\n \"hccs\": 27202,\n \"icf\": 27203,\n \"drilling\": 27204,\n \"##109\": 27205,\n \"gilts\": 27206,\n \"##arming\": 27207,\n \"topo\": 27208,\n \"camkii\": 27209,\n \"contag\": 27210,\n \"nitrification\": 27211,\n \"electroencephalographic\": 27212,\n \"urge\": 27213,\n \"bonferroni\": 27214,\n \"##udied\": 27215,\n \"##robacter\": 27216,\n \"343\": 27217,\n \"deoxyuridine\": 27218,\n \"nonoperative\": 27219,\n \"piperacillin\": 27220,\n \"##bands\": 27221,\n \"trivial\": 27222,\n \"##ucker\": 27223,\n \"##oduodenectomy\": 27224,\n \"##trich\": 27225,\n \"##urban\": 27226,\n \"planktonic\": 27227,\n \"daun\": 27228,\n \"##fetil\": 27229,\n \"adipogenic\": 27230,\n \"deferens\": 27231,\n \"worn\": 27232,\n \"explosive\": 27233,\n \"tapered\": 27234,\n \"voric\": 27235,\n \"sialyl\": 27236,\n \"intramural\": 27237,\n \"cog\": 27238,\n \"stereo\": 27239,\n \"subjectively\": 27240,\n \"spend\": 27241,\n \"inertial\": 27242,\n \"introg\": 27243,\n \"photob\": 27244,\n \"mpn\": 27245,\n \"salience\": 27246,\n \"mammal\": 27247,\n \"agp\": 27248,\n \"aspirate\": 27249,\n \"##rozole\": 27250,\n \"passes\": 27251,\n \"hydroxydopamine\": 27252,\n \"periplasmic\": 27253,\n \"breasts\": 27254,\n \"gis\": 27255,\n \"eleph\": 27256,\n \"timolol\": 27257,\n \"##iones\": 27258,\n \"alar\": 27259,\n \"monooxygenase\": 27260,\n \"pus\": 27261,\n \"compaction\": 27262,\n \"behaves\": 27263,\n \"respects\": 27264,\n \"aminoglycosides\": 27265,\n \"xii\": 27266,\n \"##q13\": 27267,\n \"demarc\": 27268,\n \"specialised\": 27269,\n \"##box\": 27270,\n \"irradiance\": 27271,\n \"premotor\": 27272,\n \"kanamycin\": 27273,\n \"gastrostomy\": 27274,\n \"eminence\": 27275,\n \"dech\": 27276,\n \"provoke\": 27277,\n \"hospitalised\": 27278,\n \"faecium\": 27279,\n \"##fh\": 27280,\n \"aeromonas\": 27281,\n \"tourniqu\": 27282,\n \"##orters\": 27283,\n \"cdk2\": 27284,\n \"flutter\": 27285,\n \"teleost\": 27286,\n \"cyp2c9\": 27287,\n \"dpat\": 27288,\n \"microcapsules\": 27289,\n \"fluorinated\": 27290,\n \"luts\": 27291,\n \"remitting\": 27292,\n \"venules\": 27293,\n \"tracks\": 27294,\n \"fog\": 27295,\n \"prebiotic\": 27296,\n \"nonpolar\": 27297,\n \"transplantations\": 27298,\n \"pericardium\": 27299,\n \"hyg\": 27300,\n \"clefts\": 27301,\n \"ptosis\": 27302,\n \"demented\": 27303,\n \"ept\": 27304,\n \"##ariasis\": 27305,\n \"hyperbil\": 27306,\n \"##oplasmin\": 27307,\n \"semis\": 27308,\n \"insensitivity\": 27309,\n \"stut\": 27310,\n \"spoken\": 27311,\n \"3t\": 27312,\n \"protozoa\": 27313,\n \"indonesia\": 27314,\n \"##ophoresis\": 27315,\n \"antinociception\": 27316,\n \"progest\": 27317,\n \"meropenem\": 27318,\n \"returns\": 27319,\n \"voriconazole\": 27320,\n \"converge\": 27321,\n \"louis\": 27322,\n \"feno\": 27323,\n \"cya\": 27324,\n \"rbf\": 27325,\n \"carved\": 27326,\n \"bootstrap\": 27327,\n \"interposition\": 27328,\n \"dioxygenase\": 27329,\n \"myositis\": 27330,\n \"underm\": 27331,\n \"depolarized\": 27332,\n \"assure\": 27333,\n \"pho\": 27334,\n \"outperforms\": 27335,\n \"reportedly\": 27336,\n \"paraneoplastic\": 27337,\n \"pyridoxal\": 27338,\n \"methanolic\": 27339,\n \"biofeedback\": 27340,\n \"##onception\": 27341,\n \"heard\": 27342,\n \"lordosis\": 27343,\n \"coarctation\": 27344,\n \"patchy\": 27345,\n \"sss\": 27346,\n \"adrs\": 27347,\n \"ahead\": 27348,\n \"p63\": 27349,\n \"harris\": 27350,\n \"papain\": 27351,\n \"##isperse\": 27352,\n \"##atalyst\": 27353,\n \"steatohepatitis\": 27354,\n \"adhesives\": 27355,\n \"mica\": 27356,\n \"enzymic\": 27357,\n \"resembl\": 27358,\n \"hads\": 27359,\n \"cholesteatoma\": 27360,\n \"opacities\": 27361,\n \"##olumbar\": 27362,\n \"lactamases\": 27363,\n \"topographical\": 27364,\n \"illuminated\": 27365,\n \"onwards\": 27366,\n \"pharyng\": 27367,\n \"##ologically\": 27368,\n \"nucleated\": 27369,\n \"impede\": 27370,\n \"daph\": 27371,\n \"congo\": 27372,\n \"neutropenic\": 27373,\n \"ethanolic\": 27374,\n \"425\": 27375,\n \"athymic\": 27376,\n \"beetles\": 27377,\n \"remind\": 27378,\n \"perikary\": 27379,\n \"protoporphyrin\": 27380,\n \"cytochromes\": 27381,\n \"amygdal\": 27382,\n \"##ropical\": 27383,\n \"pi3\": 27384,\n \"bronchoconstriction\": 27385,\n \"hyperkal\": 27386,\n \"ciliated\": 27387,\n \"spirituality\": 27388,\n \"plated\": 27389,\n \"dpa\": 27390,\n \"##yrrh\": 27391,\n \"antigenicity\": 27392,\n \"micronutrient\": 27393,\n \"podocytes\": 27394,\n \"mah\": 27395,\n \"antagonize\": 27396,\n \"feeder\": 27397,\n \"puerto\": 27398,\n \"microextraction\": 27399,\n \"directive\": 27400,\n \"532\": 27401,\n \"ileus\": 27402,\n \"apcs\": 27403,\n \"northwest\": 27404,\n \"radiopharmaceu\": 27405,\n \"elders\": 27406,\n \"brow\": 27407,\n \"perif\": 27408,\n \"collagens\": 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\"coincide\": 27453,\n \"mary\": 27454,\n \"intermed\": 27455,\n \"acyltransferase\": 27456,\n \"##amedullary\": 27457,\n \"cards\": 27458,\n \"rxr\": 27459,\n \"alm\": 27460,\n \"robustly\": 27461,\n \"penetrance\": 27462,\n \"reactants\": 27463,\n \"terminate\": 27464,\n \"crick\": 27465,\n \"clc\": 27466,\n \"pursue\": 27467,\n \"##hemisph\": 27468,\n \"cma\": 27469,\n \"##obular\": 27470,\n \"sectioning\": 27471,\n \"npr\": 27472,\n \"hostility\": 27473,\n \"510\": 27474,\n \"radii\": 27475,\n \"subthreshold\": 27476,\n \"impulses\": 27477,\n \"stabilities\": 27478,\n \"pans\": 27479,\n \"expenses\": 27480,\n \"amplifier\": 27481,\n \"amputations\": 27482,\n \"undoub\": 27483,\n \"##odiagn\": 27484,\n \"##flies\": 27485,\n \"##orelax\": 27486,\n \"hva\": 27487,\n \"minimizes\": 27488,\n \"rk\": 27489,\n \"##indin\": 27490,\n \"superco\": 27491,\n \"arisen\": 27492,\n \"≥5\": 27493,\n \"shark\": 27494,\n \"sanitation\": 27495,\n \"##etallic\": 27496,\n \"gloves\": 27497,\n 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\"municipalities\": 27542,\n \"decorated\": 27543,\n \"##held\": 27544,\n \"ordinal\": 27545,\n \"phenanthroline\": 27546,\n \"amf\": 27547,\n \"cmp\": 27548,\n \"rooted\": 27549,\n \"##otrig\": 27550,\n \"univ\": 27551,\n \"interrelated\": 27552,\n \"nonb\": 27553,\n \"cereals\": 27554,\n \"translating\": 27555,\n \"biologists\": 27556,\n \"immunostained\": 27557,\n \"nails\": 27558,\n \"halide\": 27559,\n \"pharynx\": 27560,\n \"exercising\": 27561,\n \"sero\": 27562,\n \"microliter\": 27563,\n \"##type\": 27564,\n \"illusion\": 27565,\n \"##pox\": 27566,\n \"bioluminescence\": 27567,\n \"heterotrophic\": 27568,\n \"psychophys\": 27569,\n \"flun\": 27570,\n \"dcp\": 27571,\n \"1800\": 27572,\n \"specifications\": 27573,\n \"##ethylamine\": 27574,\n \"succession\": 27575,\n \"optimism\": 27576,\n \"1p\": 27577,\n \"decoc\": 27578,\n \"cdt\": 27579,\n \"silage\": 27580,\n \"±0\": 27581,\n \"associating\": 27582,\n \"microvasculature\": 27583,\n \"metazo\": 27584,\n \"kinetically\": 27585,\n \"hydroxypropyl\": 27586,\n \"framingham\": 27587,\n \"minip\": 27588,\n \"hemif\": 27589,\n \"##aphic\": 27590,\n \"chromatic\": 27591,\n \"porcel\": 27592,\n \"hmscs\": 27593,\n \"punishment\": 27594,\n \"impar\": 27595,\n \"pallidus\": 27596,\n \"vapour\": 27597,\n \"troubles\": 27598,\n \"emotionally\": 27599,\n \"glaucomatous\": 27600,\n \"bav\": 27601,\n \"amr\": 27602,\n \"laa\": 27603,\n \"vntr\": 27604,\n \"inconsistencies\": 27605,\n \"aura\": 27606,\n \"apposition\": 27607,\n \"biphenyls\": 27608,\n \"evar\": 27609,\n \"##rocytic\": 27610,\n \"inaccess\": 27611,\n \"ppr\": 27612,\n \"eos\": 27613,\n \"nect\": 27614,\n \"dph\": 27615,\n \"n6\": 27616,\n \"##alignment\": 27617,\n \"nanostructure\": 27618,\n \"excellence\": 27619,\n \"amblyopia\": 27620,\n \"aglyc\": 27621,\n \"##inavir\": 27622,\n \"macronutr\": 27623,\n \"tungsten\": 27624,\n \"##abolism\": 27625,\n \"dimorphic\": 27626,\n \"kaw\": 27627,\n \"euglyc\": 27628,\n \"perfluoro\": 27629,\n \"cyp1a2\": 27630,\n \"pumped\": 27631,\n \"##aspinal\": 27632,\n \"subluxation\": 27633,\n \"490\": 27634,\n \"herbivores\": 27635,\n \"6r\": 27636,\n \"##uronate\": 27637,\n \"##anediol\": 27638,\n \"cecum\": 27639,\n \"cran\": 27640,\n \"hsp27\": 27641,\n \"angeles\": 27642,\n \"calculus\": 27643,\n \"periosteal\": 27644,\n \"aha\": 27645,\n \"uni\": 27646,\n \"stimulations\": 27647,\n \"cd80\": 27648,\n \"o1\": 27649,\n \"506\": 27650,\n \"stride\": 27651,\n \"recess\": 27652,\n \"catechin\": 27653,\n \"##cf\": 27654,\n \"paget\": 27655,\n \"trophoblastic\": 27656,\n \"ruptures\": 27657,\n \"delinqu\": 27658,\n \"##chard\": 27659,\n \"erm\": 27660,\n \"continually\": 27661,\n \"textile\": 27662,\n \"ablative\": 27663,\n \"thuringiensis\": 27664,\n \"vanadate\": 27665,\n \"histograms\": 27666,\n \"discol\": 27667,\n \"laterality\": 27668,\n \"aortas\": 27669,\n \"mineralocorticoid\": 27670,\n \"optimised\": 27671,\n \"mers\": 27672,\n \"fbp\": 27673,\n \"##isd\": 27674,\n \"##mers\": 27675,\n 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27719,\n \"procoagul\": 27720,\n \"exchanged\": 27721,\n \"tgfbeta\": 27722,\n \"asynchronous\": 27723,\n \"apn\": 27724,\n \"taiwanese\": 27725,\n \"gca\": 27726,\n \"mycotoxins\": 27727,\n \"sutured\": 27728,\n \"alexith\": 27729,\n \"genitalia\": 27730,\n \"ifa\": 27731,\n \"verteb\": 27732,\n \"##encaps\": 27733,\n \"britain\": 27734,\n \"localizes\": 27735,\n \"##holding\": 27736,\n \"coi\": 27737,\n \"mitigated\": 27738,\n \"peroxisomes\": 27739,\n \"fow\": 27740,\n \"suffers\": 27741,\n \"consultants\": 27742,\n \"haptoglobin\": 27743,\n \"localizing\": 27744,\n \"traf\": 27745,\n \"530\": 27746,\n \"lysosome\": 27747,\n \"philipp\": 27748,\n \"abb\": 27749,\n \"southwest\": 27750,\n \"pik\": 27751,\n \"wellness\": 27752,\n \"##wash\": 27753,\n \"332\": 27754,\n \"antiferromagnetic\": 27755,\n \"adiab\": 27756,\n \"arrhythmic\": 27757,\n \"summed\": 27758,\n \"lump\": 27759,\n \"pab\": 27760,\n \"adhering\": 27761,\n \"electrocardiography\": 27762,\n \"clips\": 27763,\n \"imidazol\": 27764,\n \"immunoprecipitated\": 27765,\n \"##wv\": 27766,\n \"##olines\": 27767,\n \"neuritis\": 27768,\n \"sure\": 27769,\n \"organochlor\": 27770,\n \"agrees\": 27771,\n \"##igenes\": 27772,\n \"##enstrual\": 27773,\n \"promyelocytic\": 27774,\n \"##weigh\": 27775,\n \"##emetic\": 27776,\n \"ima\": 27777,\n \"multipotent\": 27778,\n \"pedunc\": 27779,\n \"pugh\": 27780,\n \"##cinated\": 27781,\n \"dichloromethane\": 27782,\n \"herp\": 27783,\n \"ht2\": 27784,\n \"communicable\": 27785,\n \"glabrata\": 27786,\n \"bci\": 27787,\n \"tsc\": 27788,\n \"tumorigenicity\": 27789,\n \"serosal\": 27790,\n \"lamellae\": 27791,\n \"sacro\": 27792,\n \"##iosity\": 27793,\n \"dmi\": 27794,\n \"ecule\": 27795,\n \"##osteron\": 27796,\n \"##no3\": 27797,\n \"orienting\": 27798,\n \"hemiparesis\": 27799,\n \"gdf\": 27800,\n \"notch1\": 27801,\n \"##emal\": 27802,\n \"ross\": 27803,\n \"tentative\": 27804,\n \"zwitterionic\": 27805,\n \"patellofemoral\": 27806,\n \"dst\": 27807,\n \"csd\": 27808,\n \"##phia\": 27809,\n \"lenti\": 27810,\n \"uw\": 27811,\n \"wool\": 27812,\n \"sling\": 27813,\n \"scanners\": 27814,\n \"twisted\": 27815,\n \"proct\": 27816,\n \"shp\": 27817,\n \"phosphoprotein\": 27818,\n \"kcat\": 27819,\n \"portugal\": 27820,\n \"spark\": 27821,\n \"roof\": 27822,\n \"linkers\": 27823,\n \"drained\": 27824,\n \"##yclo\": 27825,\n \"mycophenolate\": 27826,\n \"gracil\": 27827,\n \"hyperprolactin\": 27828,\n \"podocyte\": 27829,\n \"addictive\": 27830,\n \"vep\": 27831,\n \"distally\": 27832,\n \"##uder\": 27833,\n \"radiotr\": 27834,\n \"intraductal\": 27835,\n \"plantarum\": 27836,\n \"informants\": 27837,\n \"407\": 27838,\n \"##onitoring\": 27839,\n \"vsd\": 27840,\n \"uninsured\": 27841,\n \"dbc\": 27842,\n \"agr\": 27843,\n \"attractiveness\": 27844,\n \"dermatologists\": 27845,\n \"##ontium\": 27846,\n \"thoracolumbar\": 27847,\n \"glomerulos\": 27848,\n \"teamwork\": 27849,\n \"haptic\": 27850,\n \"fio2\": 27851,\n \"outperformed\": 27852,\n \"##ivocally\": 27853,\n \"unspecific\": 27854,\n \"riton\": 27855,\n \"metalloprote\": 27856,\n \"bioavailable\": 27857,\n \"sdb\": 27858,\n \"ore\": 27859,\n \"050\": 27860,\n \"elevating\": 27861,\n \"indocyanine\": 27862,\n \"ejaculation\": 27863,\n \"wounding\": 27864,\n \"##b6\": 27865,\n \"alu\": 27866,\n \"##bachia\": 27867,\n \"rgc\": 27868,\n \"phloem\": 27869,\n \"##104\": 27870,\n \"ferul\": 27871,\n \"fluorophores\": 27872,\n \"propionic\": 27873,\n \"catfish\": 27874,\n \"narrower\": 27875,\n \"omitted\": 27876,\n \"ensured\": 27877,\n \"penins\": 27878,\n \"diffusely\": 27879,\n \"##rophied\": 27880,\n \"refugees\": 27881,\n \"##brom\": 27882,\n \"precocious\": 27883,\n \"ritonavir\": 27884,\n \"microspor\": 27885,\n \"burkitt\": 27886,\n \"augmenting\": 27887,\n \"iodinated\": 27888,\n \"##maleimide\": 27889,\n \"dormancy\": 27890,\n \"vine\": 27891,\n \"ncam\": 27892,\n \"bvdv\": 27893,\n \"books\": 27894,\n \"mgmt\": 27895,\n \"##yness\": 27896,\n \"ili\": 27897,\n \"happy\": 27898,\n \"perir\": 27899,\n \"dilemmas\": 27900,\n \"bum\": 27901,\n \"tams\": 27902,\n \"permeabilized\": 27903,\n \"enterocolitis\": 27904,\n \"bragg\": 27905,\n \"obstetrical\": 27906,\n \"meconium\": 27907,\n \"gossyp\": 27908,\n \"##oprazole\": 27909,\n \"recruits\": 27910,\n \"symbolic\": 27911,\n \"interferometer\": 27912,\n \"dcc\": 27913,\n \"319\": 27914,\n \"hamiltonian\": 27915,\n \"brackets\": 27916,\n \"##trauma\": 27917,\n \"jewish\": 27918,\n \"##ela\": 27919,\n \"pneumatic\": 27920,\n \"expiration\": 27921,\n \"pulsatility\": 27922,\n \"disproportionate\": 27923,\n \"sscp\": 27924,\n \"formulae\": 27925,\n \"gynaecological\": 27926,\n \"tep\": 27927,\n \"vertex\": 27928,\n \"pon1\": 27929,\n \"##silyl\": 27930,\n \"zikv\": 27931,\n \"rivarox\": 27932,\n \"##cu\": 27933,\n \"citalopram\": 27934,\n \"coel\": 27935,\n \"rams\": 27936,\n \"coinfection\": 27937,\n \"pallidum\": 27938,\n \"xenon\": 27939,\n \"6000\": 27940,\n \"ecori\": 27941,\n \"cryptococcus\": 27942,\n \"aorto\": 27943,\n \"peel\": 27944,\n \"reversion\": 27945,\n \"tactic\": 27946,\n \"hematomas\": 27947,\n \"parvovirus\": 27948,\n \"illegal\": 27949,\n \"##grip\": 27950,\n \"rivaroxaban\": 27951,\n \"ptfe\": 27952,\n \"transmitting\": 27953,\n \"fears\": 27954,\n \"proprioceptive\": 27955,\n \"amenorrhea\": 27956,\n \"aggrecan\": 27957,\n \"##aortic\": 27958,\n \"cmh2o\": 27959,\n \"historic\": 27960,\n \"founded\": 27961,\n \"bombesin\": 27962,\n \"metocl\": 27963,\n \"estimators\": 27964,\n \"##opramide\": 27965,\n \"dorsiflex\": 27966,\n \"trepon\": 27967,\n \"cgp\": 27968,\n \"lx\": 27969,\n \"mapks\": 27970,\n \"##hemispheric\": 27971,\n \"adnexal\": 27972,\n \"329\": 27973,\n \"1600\": 27974,\n \"vsv\": 27975,\n \"naturalistic\": 27976,\n \"##pping\": 27977,\n \"cgs\": 27978,\n \"binder\": 27979,\n \"vhl\": 27980,\n \"cleav\": 27981,\n \"japonicum\": 27982,\n \"multistep\": 27983,\n \"flanked\": 27984,\n \"baff\": 27985,\n \"chase\": 27986,\n \"systole\": 27987,\n \"nitride\": 27988,\n \"deformed\": 27989,\n \"alike\": 27990,\n \"empyema\": 27991,\n \"teratogenic\": 27992,\n \"extremes\": 27993,\n \"aquaporin\": 27994,\n \"llc\": 27995,\n \"meters\": 27996,\n \"prescribe\": 27997,\n \"uvr\": 27998,\n \"bcp\": 27999,\n \"##alys\": 28000,\n \"philadel\": 28001,\n \"dispensing\": 28002,\n \"glomerulosclerosis\": 28003,\n \"snare\": 28004,\n \"640\": 28005,\n \"potentiating\": 28006,\n \"lpc\": 28007,\n \"footprint\": 28008,\n \"alend\": 28009,\n \"histolytica\": 28010,\n \"brands\": 28011,\n \"retrieve\": 28012,\n \"##ublish\": 28013,\n \"formations\": 28014,\n \"microgravity\": 28015,\n \"wolbachia\": 28016,\n \"coin\": 28017,\n \"angiographically\": 28018,\n \"decompensation\": 28019,\n \"pyrazol\": 28020,\n \"photosensitizer\": 28021,\n \"anthelmin\": 28022,\n \"antiemetic\": 28023,\n \"hyperex\": 28024,\n \"373\": 28025,\n \"pri\": 28026,\n \"ellipso\": 28027,\n \"##ungin\": 28028,\n \"cgi\": 28029,\n \"excimer\": 28030,\n \"intercalation\": 28031,\n \"temp\": 28032,\n \"ssri\": 28033,\n \"essay\": 28034,\n \"immunodeficient\": 28035,\n \"afterload\": 28036,\n \"photophysical\": 28037,\n \"pleura\": 28038,\n \"urchin\": 28039,\n \"mosm\": 28040,\n \"paresis\": 28041,\n \"pseudop\": 28042,\n \"##encephaly\": 28043,\n \"correlational\": 28044,\n \"##terdam\": 28045,\n \"purify\": 28046,\n \"urbanization\": 28047,\n \"dodec\": 28048,\n \"titrated\": 28049,\n \"327\": 28050,\n \"hepatocarcin\": 28051,\n \"nfk\": 28052,\n \"ingrowth\": 28053,\n \"tlr9\": 28054,\n \"baboons\": 28055,\n \"karyotypes\": 28056,\n \"lifestyles\": 28057,\n \"carvedilol\": 28058,\n \"osteochondral\": 28059,\n \"disulph\": 28060,\n \"smad3\": 28061,\n \"imbalances\": 28062,\n \"##jugated\": 28063,\n \"constell\": 28064,\n \"##tage\": 28065,\n \"invasively\": 28066,\n \"banks\": 28067,\n \"postintervention\": 28068,\n \"committees\": 28069,\n \"ethidium\": 28070,\n \"mpv\": 28071,\n \"meningitidis\": 28072,\n \"cdc42\": 28073,\n 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\"ninth\": 28384,\n \"dcd\": 28385,\n \"al2o3\": 28386,\n \"##icates\": 28387,\n \"plr\": 28388,\n \"prominently\": 28389,\n \"amphibians\": 28390,\n \"##chid\": 28391,\n \"##obiology\": 28392,\n \"pyrrole\": 28393,\n \"##azolamide\": 28394,\n \"immunogold\": 28395,\n \"boiling\": 28396,\n \"microsatellites\": 28397,\n \"subfamilies\": 28398,\n \"oak\": 28399,\n \"##omyositis\": 28400,\n \"augments\": 28401,\n \"flattened\": 28402,\n \"azido\": 28403,\n \"narcol\": 28404,\n \"c10\": 28405,\n \"jac\": 28406,\n \"transfectants\": 28407,\n \"##imited\": 28408,\n \"photos\": 28409,\n \"sensitis\": 28410,\n \"ceus\": 28411,\n \"regained\": 28412,\n \"##oxifene\": 28413,\n \"##efaciens\": 28414,\n \"biologics\": 28415,\n \"##ething\": 28416,\n \"explosion\": 28417,\n \"informing\": 28418,\n \"##ocampal\": 28419,\n \"noncompl\": 28420,\n \"tdf\": 28421,\n \"hiaa\": 28422,\n \"igan\": 28423,\n \"arachnoid\": 28424,\n \"deregulated\": 28425,\n \"nanocl\": 28426,\n \"glycolipid\": 28427,\n \"adeno\": 28428,\n \"##afs\": 28429,\n \"aus\": 28430,\n \"##anoate\": 28431,\n \"multinucleated\": 28432,\n \"##oraph\": 28433,\n \"approximations\": 28434,\n \"ameliorating\": 28435,\n \"something\": 28436,\n \"motivations\": 28437,\n \"hydrolysate\": 28438,\n \"##tase\": 28439,\n \"endobronchial\": 28440,\n \"extents\": 28441,\n \"pdf\": 28442,\n \"acetylglucosamine\": 28443,\n \"boar\": 28444,\n \"l6\": 28445,\n \"txa2\": 28446,\n \"##perfusion\": 28447,\n \"##ogranin\": 28448,\n \"vesico\": 28449,\n \"ppe\": 28450,\n \"posttranscription\": 28451,\n \"hypospadias\": 28452,\n \"iκb\": 28453,\n \"mitigating\": 28454,\n \"diving\": 28455,\n \"outweigh\": 28456,\n \"##isic\": 28457,\n \"carniv\": 28458,\n \"##hipp\": 28459,\n \"inat\": 28460,\n \"penetrated\": 28461,\n \"ests\": 28462,\n \"perinuclear\": 28463,\n \"nymphs\": 28464,\n \"telangiectasia\": 28465,\n \"##ferior\": 28466,\n \"porcelain\": 28467,\n \"travers\": 28468,\n \"midwifery\": 28469,\n \"unimp\": 28470,\n 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\"aspirated\": 28559,\n \"chicago\": 28560,\n \"660\": 28561,\n \"deoxyglucose\": 28562,\n \"haematoma\": 28563,\n \"reinn\": 28564,\n \"mdi\": 28565,\n \"simplest\": 28566,\n \"412\": 28567,\n \"prf\": 28568,\n \"scand\": 28569,\n \"toronto\": 28570,\n \"armed\": 28571,\n \"exfoli\": 28572,\n \"undoubtedly\": 28573,\n \"##ascin\": 28574,\n \"dinitroph\": 28575,\n \"arthropod\": 28576,\n \"##yelinated\": 28577,\n \"dichotomous\": 28578,\n \"endotoxemia\": 28579,\n \"propanol\": 28580,\n \"archival\": 28581,\n \"nachrs\": 28582,\n \"handgrip\": 28583,\n \"contusion\": 28584,\n \"ucb\": 28585,\n \"vlbw\": 28586,\n \"nigros\": 28587,\n \"autoreactive\": 28588,\n \"numeric\": 28589,\n \"repairing\": 28590,\n \"o6\": 28591,\n \"pmt\": 28592,\n \"flt\": 28593,\n \"circr\": 28594,\n \"radiolabelled\": 28595,\n \"pmp\": 28596,\n \"complements\": 28597,\n \"eot\": 28598,\n \"##omatoid\": 28599,\n \"noble\": 28600,\n \"339\": 28601,\n \"##dlers\": 28602,\n \"damping\": 28603,\n \"mall\": 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\"irritability\": 28649,\n \"##iced\": 28650,\n \"immunol\": 28651,\n \"##ogly\": 28652,\n \"remissions\": 28653,\n \"##tening\": 28654,\n \"digitor\": 28655,\n \"##itica\": 28656,\n \"intratracheal\": 28657,\n \"51cr\": 28658,\n \"distractor\": 28659,\n \"tdr\": 28660,\n \"##omicroscopy\": 28661,\n \"beside\": 28662,\n \"5mg\": 28663,\n \"overexpress\": 28664,\n \"fishing\": 28665,\n \"situational\": 28666,\n \"mog\": 28667,\n \"distinguishes\": 28668,\n \"appendage\": 28669,\n \"multiplexed\": 28670,\n \"srebp\": 28671,\n \"accelerator\": 28672,\n \"##enesulf\": 28673,\n \"dependencies\": 28674,\n \"fragilis\": 28675,\n \"monoclinic\": 28676,\n \"confounded\": 28677,\n \"dang\": 28678,\n \"neurologically\": 28679,\n \"invariance\": 28680,\n \"stakeholder\": 28681,\n \"mosaicism\": 28682,\n \"tartrate\": 28683,\n \"##metry\": 28684,\n \"elapsed\": 28685,\n \"##eptidyl\": 28686,\n \"symbiosis\": 28687,\n \"##hz\": 28688,\n \"prolyl\": 28689,\n \"laminectomy\": 28690,\n \"consulting\": 28691,\n \"crm\": 28692,\n \"mgo\": 28693,\n \"nucleosomes\": 28694,\n \"autoc\": 28695,\n \"##ethoxy\": 28696,\n \"7a\": 28697,\n \"adiabatic\": 28698,\n \"vre\": 28699,\n \"odors\": 28700,\n \"succinyl\": 28701,\n \"hyperhom\": 28702,\n \"kilobase\": 28703,\n \"##obenzyl\": 28704,\n \"monomethyl\": 28705,\n \"xylanase\": 28706,\n \"##ibrin\": 28707,\n \"removable\": 28708,\n \"biting\": 28709,\n \"overlaps\": 28710,\n \"addicts\": 28711,\n \"turp\": 28712,\n \"multich\": 28713,\n \"##aco\": 28714,\n \"suppressors\": 28715,\n \"reappear\": 28716,\n \"deceleration\": 28717,\n \"catalyzing\": 28718,\n \"cauda\": 28719,\n \"##5ac\": 28720,\n \"dips\": 28721,\n \"nj\": 28722,\n \"##ager\": 28723,\n \"appliances\": 28724,\n \"scalar\": 28725,\n \"desaturase\": 28726,\n \"artemisinin\": 28727,\n \"vec\": 28728,\n \"##uloplasmin\": 28729,\n \"perforator\": 28730,\n \"kindling\": 28731,\n \"364\": 28732,\n \"orchid\": 28733,\n \"tetrahedral\": 28734,\n \"enoxaparin\": 28735,\n \"trac\": 28736,\n \"corynebacterium\": 28737,\n \"##attern\": 28738,\n \"photovoltaic\": 28739,\n \"electricity\": 28740,\n \"diffusing\": 28741,\n \"p15\": 28742,\n \"abca1\": 28743,\n \"oct4\": 28744,\n \"auc0\": 28745,\n \"ero\": 28746,\n \"##qx\": 28747,\n \"##ithi\": 28748,\n \"19th\": 28749,\n \"atpases\": 28750,\n \"desiccation\": 28751,\n \"##idinyl\": 28752,\n \"mre\": 28753,\n \"respondent\": 28754,\n \"rehydration\": 28755,\n \"conclusively\": 28756,\n \"medulloblastoma\": 28757,\n \"advocates\": 28758,\n \"pme\": 28759,\n \"biophys\": 28760,\n \"infiltrative\": 28761,\n \"stereois\": 28762,\n \"settlement\": 28763,\n \"dr4\": 28764,\n \"barth\": 28765,\n \"gpc\": 28766,\n \"mentors\": 28767,\n \"spd\": 28768,\n \"phenanthrene\": 28769,\n \"##ablation\": 28770,\n \"##craft\": 28771,\n \"cryotherapy\": 28772,\n \"≥10\": 28773,\n \"decarbox\": 28774,\n \"scrapie\": 28775,\n \"##trium\": 28776,\n \"hyperuricemia\": 28777,\n \"ata\": 28778,\n \"mp2\": 28779,\n \"##empfer\": 28780,\n \"##egm\": 28781,\n \"orthologous\": 28782,\n \"facultative\": 28783,\n \"bom\": 28784,\n \"precancerous\": 28785,\n \"##cytidine\": 28786,\n \"diamine\": 28787,\n \"pao\": 28788,\n \"repeatable\": 28789,\n \"chimera\": 28790,\n \"communicative\": 28791,\n \"mnsod\": 28792,\n \"preadip\": 28793,\n \"milligr\": 28794,\n \"figo\": 28795,\n \"igg2a\": 28796,\n \"n0\": 28797,\n \"bifidobacterium\": 28798,\n \"evidently\": 28799,\n \"##hf\": 28800,\n \"dendrimer\": 28801,\n \"leprae\": 28802,\n \"digitorum\": 28803,\n \"menten\": 28804,\n \"saponin\": 28805,\n \"mould\": 28806,\n \"spawning\": 28807,\n \"dpc\": 28808,\n \"pentyl\": 28809,\n \"##enk\": 28810,\n \"chur\": 28811,\n \"ensembles\": 28812,\n \"neuroleptics\": 28813,\n \"##osfamide\": 28814,\n \"absor\": 28815,\n \"##v6\": 28816,\n \"octahedral\": 28817,\n \"##answ\": 28818,\n \"pulmon\": 28819,\n \"356\": 28820,\n \"opacification\": 28821,\n \"##adders\": 28822,\n \"##aglobulin\": 28823,\n \"tourniquet\": 28824,\n \"enema\": 28825,\n \"ferred\": 28826,\n \"forget\": 28827,\n \"nonverbal\": 28828,\n \"iva\": 28829,\n \"352\": 28830,\n \"meticulous\": 28831,\n \"abscisic\": 28832,\n \"burkholderia\": 28833,\n \"gii\": 28834,\n \"influent\": 28835,\n \"##pots\": 28836,\n \"botan\": 28837,\n \"348\": 28838,\n \"dihydrox\": 28839,\n \"micrometast\": 28840,\n \"digitized\": 28841,\n \"rs22\": 28842,\n \"lips\": 28843,\n \"pfo\": 28844,\n \"igd\": 28845,\n \"visitors\": 28846,\n \"cd11c\": 28847,\n \"lights\": 28848,\n \"emf\": 28849,\n \"##engine\": 28850,\n \"ethm\": 28851,\n \"chlorpyr\": 28852,\n \"cisterna\": 28853,\n \"##opyranoside\": 28854,\n \"autoradiographic\": 28855,\n \"perchlorate\": 28856,\n \"synonym\": 28857,\n \"##ercise\": 28858,\n \"stereochemistry\": 28859,\n \"##wall\": 28860,\n \"dyslexia\": 28861,\n \"excursion\": 28862,\n \"tesla\": 28863,\n \"autophosphorylation\": 28864,\n \"##ipin\": 28865,\n \"upar\": 28866,\n \"resurf\": 28867,\n \"sterilized\": 28868,\n \"granulomatosis\": 28869,\n \"µmol\": 28870,\n \"eicosapentaenoic\": 28871,\n \"##castle\": 28872,\n \"##ecretion\": 28873,\n \"structuring\": 28874,\n \"##phys\": 28875,\n \"bulbar\": 28876,\n \"ketoac\": 28877,\n \"sydney\": 28878,\n \"mnc\": 28879,\n \"extramedullary\": 28880,\n \"rhu\": 28881,\n \"tropism\": 28882,\n \"hypophosph\": 28883,\n \"clarifying\": 28884,\n \"judgement\": 28885,\n \"jord\": 28886,\n \"puzz\": 28887,\n \"diplopia\": 28888,\n \"deoxynucleotidyl\": 28889,\n \"lympho\": 28890,\n \"##alogy\": 28891,\n \"##6c\": 28892,\n \"clothing\": 28893,\n \"tph\": 28894\n }\n }\n}"
},
{
"path": "Quick_demo/MedKEBERT/tokenizer_config.json",
"content": "{\n \"cls_token\": \"[CLS]\",\n \"do_basic_tokenize\": true,\n \"do_lower_case\": true,\n \"mask_token\": \"[MASK]\",\n \"name_or_path\": \"xmcmic/Med-KEBERT\",\n \"never_split\": null,\n \"pad_token\": \"[PAD]\",\n \"sep_token\": \"[SEP]\",\n \"special_tokens_map_file\": null,\n \"strip_accents\": null,\n \"tokenize_chinese_chars\": true,\n \"tokenizer_class\": \"BertTokenizer\",\n \"unk_token\": \"[UNK]\"\n}\n"
},
{
"path": "Quick_demo/MedKEBERT/vocab.txt",
"content": "[PAD]\n[UNK]\n[CLS]\n[SEP]\n[MASK]\n!\n#\n$\n%\n&\n'\n(\n)\n*\n+\n,\n-\n.\n/\n0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n:\n;\n<\n=\n>\n?\n@\n[\n\\\n]\n^\n_\n`\na\nb\nc\nd\ne\nf\ng\nh\ni\nj\nk\nl\nm\nn\no\np\nq\nr\ns\nt\nu\nv\nw\nx\ny\nz\n{\n|\n}\n~\n¡\n¢\n£\n¤\n¥\n¦\n§\n¨\n©\nª\n«\n¬\n®\n¯\n°\n±\n²\n³\n´\nµ\n¶\n·\n¸\n¹\nº\n»\n¼\n½\n¾\n¿\n×\nß\næ\nð\n÷\nø\nþ\nđ\nħ\nı\nĸ\nł\nŋ\nœ\nƅ\nƈ\nƍ\nƒ\nƙ\nƛ\nƞ\nƭ\nƴ\nƶ\nƿ\nǀ\nǁ\nǂ\nǝ\nȣ\nȥ\nȵ\nȶ\nȼ\nɐ\nɑ\nɒ\nɓ\nɔ\nɕ\nɖ\nə\nɚ\nɛ\nɝ\nɡ\nɣ\nɤ\nɥ\nɨ\nɩ\nɪ\nɬ\nɭ\nɮ\nɯ\nɲ\nɳ\nɵ\nɷ\nɸ\nɹ\nɻ\nɾ\nɿ\nʀ\nʁ\nʂ\nʃ\nʅ\nʈ\nʉ\nʊ\nʋ\nʌ\nʎ\nʐ\nʑ\nʒ\nʔ\nʕ\nʘ\nʝ\nʟ\nʦ\nʧ\nʰ\nʱ\nʲ\nʷ\nʹ\nʺ\nʼ\nʾ\nˁ\n˂\n˃\n˄\nˆ\nˇ\nˉ\nː\nˑ\n˖\n˘\n˙\n˚\n˜\n˝\n˞\n˟\nˠ\nˤ\n˪\n˭\nˮ\n˴\nͳ\nͻ\n\n\n\n\n΄\n\nα\nβ\nγ\nδ\nε\nζ\nη\nθ\nι\nκ\nλ\nμ\nν\nξ\nο\nπ\nρ\nς\nσ\nτ\nυ\nφ\nχ\nψ\nω\nϐ\nϑ\nϒ\nϕ\nϖ\nϝ\nϫ\nϭ\nϰ\nϱ\nϲ\nϵ\nϻ\nϼ\nа\nб\nв\nг\nд\nе\nж\nз\nи\nк\nл\nм\nн\nо\nп\nр\nс\nт\nу\nф\nх\nц\nч\nш\nщ\nь\nэ\nю\nя\nє\nѕ\nі\nј\nћ\nѱ\nѳ\nѵ\nґ\nқ\nҝ\nҡ\nҫ\nү\nұ\nҳ\nһ\nӏ\nӕ\nә\nө\nӽ\nԏ\nԑ\n՚\n־\nא\nי\nץ\nר\nש\n׳\n״\n،\nا\nة\nت\nح\nخ\nد\nر\nز\nش\nط\nع\nـ\nف\nل\nم\nو\n٠\n١\n٢\n٤\n٪\n٭\nۥ\nߚ\nߝ\nࣈ\nक\nच\nण\nफ\nर\nल\nा\n०\nก\nข\nง\nต\nท\nน\nบ\nพ\nฟ\nภ\nย\nร\nล\nว\nศ\nห\nอ\nะ\nา\n฿\nแ\nใ\n༌\nခ\nᄀ\nᄁ\nᄂ\nᄃ\nᄄ\nᄅ\nᄆ\nᄇ\nᄉ\nᄋ\nᄌ\nᄎ\nᄏ\nᄐ\nᄑ\nᄒ\nᅟ\nᅡ\nᅢ\nᅣ\nᅥ\nᅦ\nᅧ\nᅨ\nᅩ\nᅪ\nᅬ\nᅭ\nᅮ\nᅯ\nᅰ\nᅱ\nᅲ\nᅳ\nᅴ\nᅵ\nᆨ\nᆩ\nᆪ\nᆫ\nᆭ\nᆯ\nᆲ\nᆷ\nᆸ\nᆺ\nᆻ\nᆼ\nᆾ\nᇀ\nᇂ\nᇞ\n᛫\nᴂ\nᴅ\nᴋ\nᴍ\nᴏ\nᴐ\nᴓ\nᴨ\nᴪ\nᴳ\nᴼ\nᵒ\nᵗ\nᵝ\nᵧ\nᵪ\nᵯ\nᵹ\nᶲ\n᾽\n᾿\n῾\n‑\n‒\n—\n―\n‖\n‘\n’\n‚\n‛\n“\n”\n„\n‟\n†\n‡\n•\n‥\n…\n‧\n‰\n‱\n′\n″\n‴\n‹\n›\n※\n‾\n‿\n⁁\n⁃\n⁄\n⁎\n⁓\n⁗\n⁰\nⁱ\n⁴\n⁵\n⁶\n⁷\n⁸\n⁹\n⁺\n⁻\n⁽\n⁾\nⁿ\n₀\n₁\n₂\n₃\n₄\n₅\n₆\n₇\n₈\n₉\n₋\n₌\nₐ\nₓ\n₣\n₤\n₦\n₩\n€\n₱\n₵\n₹\n₺\nℂ\n℃\n℅\nℇ\nℋ\nℏ\nℐ\nℑ\nℒ\nℓ\nℕ\n№\n℗\nℙ\nℛ\nℜ\nℝ\n℞\n℠\n™\nℤ\n℧\nℬ\nℰ\nℱ\nℳ\nℴ\nℵ\nℽ\nⅅ\n⅓\n⅔\n⅗\n⅙\n⅚\n⅛\n⅜\nⅰ\nⅱ\nⅲ\nⅳ\nⅴ\nⅵ\nⅶ\nⅷ\nⅸ\nⅹ\nⅺ\nⅻ\n←\n↑\n→\n↓\n↔\n↕\n↗\n↘\n↙\n↝\n↦\n↷\n↼\n⇀\n⇄\n⇆\n⇋\n⇌\n⇐\n⇑\n⇒\n⇓\n⇔\n⇝\n⇨\n∀\n∂\n∅\n∆\n∇\n∈\n∊\n∋\n∎\n∏\n∐\n∑\n−\n∓\n∕\n∖\n∗\n∘\n∙\n√\n∛\n∝\n∞\n∟\n∠\n∡\n∢\n∣\n∥\n∧\n∨\n∩\n∪\n∫\n∴\n∶\n∷\n∸\n∼\n∽\n∾\n≂\n≃\n≅\n≈\n≊\n≌\n≍\n≏\n≐\n≑\n≒\n≔\n≙\n≡\n≣\n≤\n≥\n≦\n≧\n≨\n≪\n≫\n≲\n≳\n≺\n≻\n≽\n≿\n⊂\n⊃\n⊆\n⊕\n⊖\n⊗\n⊘\n⊙\n⊞\n⊟\n⊠\n⊣\n⊤\n⊥\n⊿\n⋄\n⋅\n⋆\n⋊\n⋘\n⋙\n⋜\n⋝\n⋮\n⋯\n⌀\n⌈\n⌉\n⌊\n⌋\n⌜\n⌝\n⌢\n⌣\n⍴\n⍵\n⍺\n⎕\n⎼\n␣\n①\n②\n③\n④\n⑤\n⑥\n⑦\n⑧\n⑨\n⑩\n⑴\n⑵\n⑶\nⓒ\nⓝ\nⓡ\n─\n│\n├\n┤\n┬\n┴\n═\n║\n╪\n╳\n█\n░\n▒\n▓\n■\n□\n▪\n▫\n▬\n▯\n▲\n△\n▴\n▵\n▶\n▸\n▹\n►\n▼\n▽\n▾\n▿\n◆\n◇\n◊\n○\n●\n◦\n◻\n◽\n★\n☆\n☉\n☐\n☓\n☺\n♀\n♂\n♢\n♦\n♪\n♭\n♯\n✓\n✕\n✜\n✧\n✴\n✶\n➁\n➔\n➝\n➢\n➤\n⟂\n⟦\n⟧\n⟨\n⟩\n⟵\n⟶\n⦁\n⦵\n⧧\n⧸\n⧹\n⨉\n⨪\n⨯\n⩒\n⩼\n⩽\n⩾\n⩿\n⪅\n⪆\n⪕\n⪖\n⪝\n⪡\n⪢\n⫽\n⬄\n⬜\n⬡\n⬢\n⬰\n⬽\nⱪ\n⸱\n⿿\n、\n。\n〈\n〉\n《\n》\n「\n」\n【\n】\n〓\n〔\n〕\n〖\n〗\n〜\n〝\n〟\nア\nカ\nヒ\nメ\nリ\nレ\n・\nㅣ\nㆍ\n㎂\n㎍\n㎖\n㎛\n㎝\n㎟\n㎡\n㎶\n㒐\n丸\n参\n囊\n大\n射\n方\n气\n汤\n注\n消\n液\n清\n潜\n瘀\n益\n祛\n粒\n肝\n肾\n胶\n芪\n补\n颗\n饮\n骨\n꞉\n꞊\nꞌ\nꞵ\nff\nfi\nfl\nffi\nffl\nſt\n﴾\n﴿\n︰\n﹒\n﹛\n﹢\n﹣\n﹤\n﹥\n﹩\n＄\n％\n＆\n＇\n（\n）\n＊\n＋\n，\n－\n．\n：\n；\n＜\n＝\n＞\n？\n［\n］\n＾\n＿\nｃ\nｉ\nｍ\nｐ\nｖ\n｛\n｜\n｝\n～\n､\n･\nｱ\nｵ\nﾀ\nﾓ\nￗ\n￡\n￢\n￥\n￦\n\n𝐑\n𝐟\n𝐫\n𝐴\n𝐸\n𝑃\n𝑐\n𝑑\n𝑒\n𝑓\n𝑖\n𝑛\n𝑜\n𝑟\n𝑡\n𝑥\n𝒆\n𝒙\n𝒞\n𝒟\n𝒦\n𝒩\n𝒪\n𝒫\n𝒮\n𝒯\n𝓟\n𝓣\n𝔇\n𝔐\n𝔹\n𝕊\n𝕜\n𝖱\n𝘗\n𝛂\n𝛆\n𝛼\n𝛽\n𝜀\n𝜃\n𝜅\n𝜇\n𝜋\n𝜌\n𝜒\n𝜖\n𝜗\n𝝁\n##y\n##q\n##e\n##0\n##5\n##3\n##8\n##a\n##g\n##p\n##c\n##t\n##4\n##6\n##7\n##2\n##h\n##d\n##i\n##n\n##o\n##s\n##r\n##w\n##u\n##k\n##b\n##f\n##l\n##m\n##z\n##v\n##1\n##9\n##⋮\n##°\n##x\n##j\n##≈\n##₃\n##∙\n##λ\n##⁻\n##μ\n##ø\n##∼\n##→\n##δ\n##×\n##™\n##∕\n##≫\n##β\n##±\n##₂\n##₆\n##⩽\n##€\n##®\n##∓\n##α\n##с\n##γ\n##£\n##≡\n##⋯\n##π\n##═\n##⋆\n##ρ\n##µ\n##σ\n##₅\n##₄\n##˚\n##ᅴ\n##ᄇ\n##ᅡ\n##ᆼ\n##ᄋ\n##ᅲ\n##ᄌ\n##ᅱ\n##₋\n##fi\n##´\n##₇\n##ε\n##ß\n##＋\n##η\n##©\n##б\n##³\n##е\n##ı\n##□\n##∗\n##∶\n##⁄\n##−\n##ϕ\n##⊂\n##˙\n##º\n##∆\n##↓\n##κ\n##∞\n##⁺\n##₈\n##fl\n##ι\n##⁸\n##∷\n##⋅\n##²\n##⁰\n##æ\n##↔\n##₁\n##ζ\n##τ\n##÷\n##＝\n##∘\n##¹\n##⁷\n##⁶\n##χ\n##ω\n##ϒ\n##ℏ\n##ˆ\n##ф\n##о\n##↑\n##⩾\n##φ\n##¬\n##ϵ\n##υ\n##₉\n##θ\n##ψ\n##─\n##├\n##≪\n##ł\n##♂\n##ℓ\n##➔\n##ⅱ\n##⁴\n##≳\n##ν\n##～\n##√\n##≲\n##＜\n##к\n##⁹\n##⊃\n##н\n##∝\n##₀\n##⁵\n##¯\n##←\n##ff\n##΄\n##а\n##ə\n##◦\n##≃\n##≅\n##œ\n##ɛ\n##⧹\n##⍺\n##ⅰ\n##ɑ\n##ο\n##⇒\n##♀\n##˂\n##○\n##▴\n##＞\n##⊗\n##р\n##в\n##ж\n##℃\n##∈\n##∧\n##⊙\n##¢\n##м\n##ᅭ\n##ᅵ\n##ᆫ\n##ᅦ\n##∑\n##у\n##з\n##л\n##і\n##≦\n##≧\n##ˮ\n##≒\n##﹥\n##≊\n##¼\n##△\n##ᄅ\n##ᅩ\n##ᅥ\n##ᆨ\n##đ\n##⊥\n##т\n##ⅳ\n##˃\n##ƒ\n##ξ\n##│\n##¾\n##ⅲ\n##ð\n##ϲ\n##ⓒ\n##␣\n##┴\n##∥\n##▪\n##⧧\n##𝛽\n##➝\n##ɣ\n##∇\n##ш\n##ᶲ\n##и\n##г\n##♦\n##∫\n##∩\n##½\n##ſt\n##⇋\n##ⅷ\n##˜\n##⧸\n##¨\n##⨯\n##∣\n##ª\n##∨\n##ĸ\n##●\n##▬\n##¥\n##ⅴ\n##⇑\n##⇓\n##⇆\n##⬢\n##∏\n##ɕ\n##℅\n##х\n##⇌\n##ː\n##∪\n##⊕\n##ǀ\n##˖\n##ɒ\n##⬜\n##ᆯ\n##ᆸ\n##д\n##𝐑\n##⇀\n##˝\n##≐\n##þ\n##↷\n##ɹ\n##ʃ\n##ƞ\n##⟶\n##⇄\n##┤\n##ц\n##я\n##ߚ\n##¸\n##п\n##∴\n##⇔\n##№\n##ʱ\n##↦\n##ͳ\n##⪅\n##▵\n##⇝\n##ɚ\n##ᆺ\n##ɐ\n##ɪ\n##②\n##ҡ\n##⊠\n##∂\n##ꞌ\n##≍\n##ˇ\n##▒\n##ю\n##☓\n##∖\n##ˉ\n##┬\n##⎼\n##ₓ\n##ς\n##〓\n##¦\n##ᵒ\n##⋘\n##ⁱ\n##ᆷ\n##ᅳ\n##⅓\n##㎍\n##■\n##♯\n##\n##≿\n##ү\n##ᄉ\n##ϭ\n##˞\n##ɸ\n##∅\n##⪢\n##↝\n##ᴂ\n##𝛆\n##ᵝ\n##˄\n##ᵧ\n##ᅪ\n##ᄒ\n##ᄀ\n##ᅧ\n##ᄃ\n##∊\n##ᅟ\n##¤\n##☉\n##ʰ\n##ⓡ\n##ᄆ\n##ϐ\n##ר\n##א\n##ש\n##י\n##｜\n##▓\n##ч\n##ь\n##ᄑ\n##ᆩ\n##ᄂ\n##ˑ\n##⊖\n##⦁\n##ℑ\n##ℇ\n##ⅺ\n##⋜\n##᾿\n##ᅢ\n##￥\n##║\n##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},
{
"path": "Quick_demo/Model/RadFM/__init__.py",
"content": ""
},
{
"path": "Quick_demo/Model/RadFM/blocks.py",
"content": "from collections import OrderedDict\nfrom typing import Tuple, Union, Callable, Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torch.utils.checkpoint import checkpoint\n\nclass PMC_CLIP_cfg:\n backbone: str = 'ModifiedRN50' # ['RN50', 'ModifiedRN50', 'MAE']\n layers: Union[Tuple[int, int, int, int], int] = [3,4,6,3]\n width: int = 64\n head_width: int = 64\n mlp_ratio: float = 4.0\n patch_size: int = 16\n image_size: Union[Tuple[int, int], int] = 224\n timm_model_name: str = None # a valid model name overrides layers, width, patch_size\n timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model\n timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')\n timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '')\n patch_dropout: float = 0.0 # patch dropout rate, no dropout by default\n drop_attention_rate: float = 0. # Transformer Dropout\n patch_size: None\n \nclass Bottleneck(nn.Module):\n expansion = 4\n\n def __init__(self, inplanes, planes, stride=1):\n super().__init__()\n\n # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1\n self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.relu1 = nn.ReLU(inplace=True)\n\n self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(planes)\n self.relu2 = nn.ReLU(inplace=True)\n\n self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()\n\n self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)\n self.bn3 = nn.BatchNorm2d(planes * self.expansion)\n self.relu3 = nn.ReLU(inplace=True)\n\n self.downsample = None\n self.stride = stride\n\n if stride > 1 or inplanes != planes * Bottleneck.expansion:\n # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1\n self.downsample = nn.Sequential(OrderedDict([\n (\"-1\", nn.AvgPool2d(stride)),\n (\"0\", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),\n (\"1\", nn.BatchNorm2d(planes * self.expansion))\n ]))\n\n def forward(self, x: torch.Tensor):\n identity = x\n\n out = self.relu1(self.bn1(self.conv1(x)))\n out = self.relu2(self.bn2(self.conv2(out)))\n out = self.avgpool(out)\n out = self.bn3(self.conv3(out))\n\n if self.downsample is not None:\n identity = self.downsample(x)\n\n out += identity\n out = self.relu3(out)\n return out\n\n\nclass AttentionPool2d(nn.Module):\n def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):\n super().__init__()\n self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)\n self.k_proj = nn.Linear(embed_dim, embed_dim)\n self.q_proj = nn.Linear(embed_dim, embed_dim)\n self.v_proj = nn.Linear(embed_dim, embed_dim)\n self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)\n self.num_heads = num_heads\n\n def forward(self, x):\n x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC\n x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC\n x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC\n x, _ = F.multi_head_attention_forward(\n query=x, key=x, value=x,\n embed_dim_to_check=x.shape[-1],\n num_heads=self.num_heads,\n q_proj_weight=self.q_proj.weight,\n k_proj_weight=self.k_proj.weight,\n v_proj_weight=self.v_proj.weight,\n in_proj_weight=None,\n in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),\n bias_k=None,\n bias_v=None,\n add_zero_attn=False,\n dropout_p=0,\n out_proj_weight=self.c_proj.weight,\n out_proj_bias=self.c_proj.bias,\n use_separate_proj_weight=True,\n training=self.training,\n need_weights=False\n )\n\n return x[0]\n\n\nclass ResNet(nn.Module):\n \"\"\"\n RN50\n \"\"\"\n\n def __init__(\n self, layers, output_dim, heads, image_size=224, width=64,\n block=Bottleneck,\n ):\n super().__init__()\n self.output_dim = output_dim\n self.image_size = image_size\n\n # the 1-layer stem\n self.conv1 = nn.Conv2d(3, width, kernel_size=3, stride=2, padding=1, bias=False)\n self.bn1 = nn.BatchNorm2d(width)\n self.relu1 = nn.ReLU(inplace=True)\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n # residual layers\n self._inplanes = width # this is a *mutable* variable used during construction\n self.layer1 = self._make_layer(width, layers[0])\n self.layer2 = self._make_layer(width * 2, layers[1], stride=2)\n self.layer3 = self._make_layer(width * 4, layers[2], stride=2)\n self.layer4 = self._make_layer(width * 8, layers[3], stride=2)\n self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n # self.head = nn.Linear(512 * 6, output_dim)\n self.head = nn.Linear(512 * block.expansion, output_dim)\n\n # embed_dim = width * 32 # the ResNet feature dimension\n # self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)\n\n self.init_parameters()\n\n def _make_layer(\n self,\n planes, blocks, stride=1,\n block=Bottleneck,\n ):\n layers = [block(self._inplanes, planes, stride)]\n\n self._inplanes = planes * block.expansion\n for _ in range(1, blocks):\n layers.append(block(self._inplanes, planes))\n\n return nn.Sequential(*layers)\n\n def init_parameters(self):\n for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:\n for name, param in resnet_block.named_parameters():\n if name.endswith(\"bn3.weight\"):\n nn.init.zeros_(param)\n\n def lock(self, unlocked_groups=0, freeze_bn_stats=False):\n assert unlocked_groups == 0, 'partial locking not currently supported for this model'\n for param in self.parameters():\n param.requires_grad = False\n if freeze_bn_stats:\n freeze_batch_norm_2d(self)\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n # FIXME support for non-transformer\n pass\n\n def stem(self, x):\n x = self.relu1(self.bn1(self.conv1(x)))\n x = self.maxpool(x)\n return x\n\n def forward(self, x):\n # x[0]: [batch_size, 3, 224, 224]\n # x[1]: [batch_size, 1]\n x = self.stem(x) # [batch_size, 64, 56, 56]\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x) # [batch_size, 2048, 7, 7]\n x = self.avgpool(x) # [batch_size, 2048, 1, 1]\n x = torch.flatten(x, 1) # [batch_size, 2048*1*1]\n x = self.head(x) # [batch_size, 1024]\n\n visual_output = dict.fromkeys([\"image_features\", \"mim_loss\"], None)\n visual_output.update({\n 'image_features': x,\n })\n\n return visual_output\n\n\nclass ModifiedResNet(nn.Module):\n \"\"\"\n A ResNet class that is similar to torchvision's but contains the following changes:\n - There are now 3 \"stem\" convolutions as opposed to 1, with an average pool instead of a max pool.\n - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1\n - The final pooling layer is a QKV attention instead of an average pool\n \"\"\"\n\n def __init__(self, layers, output_dim, heads, image_size=224, width=64):\n super().__init__()\n self.output_dim = output_dim\n self.image_size = image_size\n\n # the 3-layer stem\n self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)\n self.bn1 = nn.BatchNorm2d(width // 2)\n self.relu1 = nn.ReLU(inplace=True)\n self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(width // 2)\n self.relu2 = nn.ReLU(inplace=True)\n self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)\n self.bn3 = nn.BatchNorm2d(width)\n self.relu3 = nn.ReLU(inplace=True)\n self.avgpool = nn.AvgPool2d(2)\n\n # residual layers\n self._inplanes = width # this is a *mutable* variable used during construction\n self.layer1 = self._make_layer(width, layers[0])\n self.layer2 = self._make_layer(width * 2, layers[1], stride=2)\n self.layer3 = self._make_layer(width * 4, layers[2], stride=2)\n self.layer4 = self._make_layer(width * 8, layers[3], stride=2)\n\n embed_dim = width * 32 # the ResNet feature dimension\n self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)\n\n self.init_parameters()\n\n def _make_layer(self, planes, blocks, stride=1):\n layers = [Bottleneck(self._inplanes, planes, stride)]\n\n self._inplanes = planes * Bottleneck.expansion\n for _ in range(1, blocks):\n layers.append(Bottleneck(self._inplanes, planes))\n\n return nn.Sequential(*layers)\n\n def init_parameters(self):\n if self.attnpool is not None:\n std = self.attnpool.c_proj.in_features ** -0.5\n nn.init.normal_(self.attnpool.q_proj.weight, std=std)\n nn.init.normal_(self.attnpool.k_proj.weight, std=std)\n nn.init.normal_(self.attnpool.v_proj.weight, std=std)\n nn.init.normal_(self.attnpool.c_proj.weight, std=std)\n\n for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:\n for name, param in resnet_block.named_parameters():\n if name.endswith(\"bn3.weight\"):\n nn.init.zeros_(param)\n\n def lock(self, unlocked_groups=0, freeze_bn_stats=False):\n assert unlocked_groups == 0, 'partial locking not currently supported for this model'\n for param in self.parameters():\n param.requires_grad = False\n if freeze_bn_stats:\n freeze_batch_norm_2d(self)\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n # FIXME support for non-transformer\n pass\n\n def stem(self, x):\n x = self.relu1(self.bn1(self.conv1(x)))\n x = self.relu2(self.bn2(self.conv2(x)))\n x = self.relu3(self.bn3(self.conv3(x)))\n x = self.avgpool(x)\n return x\n\n def forward(self, x):\n x = self.stem(x)\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n x = self.attnpool(x)\n\n visual_output = dict.fromkeys([\"image_features\", \"mim_loss\"], None)\n visual_output.update({\n 'image_features': x,\n })\n\n return visual_output\n\n\nclass LayerNorm(nn.LayerNorm):\n \"\"\"Subclass torch's LayerNorm to handle fp16.\"\"\"\n\n def forward(self, x: torch.Tensor):\n orig_type = x.dtype\n x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)\n return x.to(orig_type)\n\n\nclass QuickGELU(nn.Module):\n # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\n\nclass ResidualAttentionBlock(nn.Module):\n def __init__(\n self, d_model: int, n_head: int, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU,\n drop_attention_rate: float = 0.,\n ):\n super().__init__()\n\n self.attn = nn.MultiheadAttention(\n embed_dim=d_model,\n num_heads=n_head,\n dropout=drop_attention_rate,\n )\n self.ln_1 = LayerNorm(d_model)\n mlp_width = int(d_model * mlp_ratio)\n self.mlp = nn.Sequential(OrderedDict([\n (\"c_fc\", nn.Linear(d_model, mlp_width)),\n (\"gelu\", act_layer()),\n (\"c_proj\", nn.Linear(mlp_width, d_model))\n ]))\n self.ln_2 = LayerNorm(d_model)\n\n def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):\n return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0]\n\n def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):\n x = x + self.attention(self.ln_1(x), attn_mask=attn_mask)\n x = x + self.mlp(self.ln_2(x))\n return x\n\n\nclass PatchDropout(nn.Module):\n \"\"\"\n https://arxiv.org/abs/2212.00794\n \"\"\"\n\n def __init__(self, prob, exclude_first_token=True):\n super().__init__()\n assert 0 <= prob < 1.\n self.prob = prob\n self.exclude_first_token = exclude_first_token # exclude CLS token\n\n def forward(self, x):\n if not self.training or self.prob == 0.:\n return x\n\n if self.exclude_first_token:\n cls_tokens, x = x[:, :1], x[:, 1:]\n else:\n cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1])\n\n batch = x.size()[0]\n num_tokens = x.size()[1]\n\n batch_indices = torch.arange(batch)\n batch_indices = batch_indices[..., None]\n\n keep_prob = 1 - self.prob\n num_patches_keep = max(1, int(num_tokens * keep_prob))\n\n rand = torch.randn(batch, num_tokens)\n patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices\n\n x = x[batch_indices, patch_indices_keep]\n\n if self.exclude_first_token:\n x = torch.cat((cls_tokens, x), dim=1)\n\n return x\n\n\nclass Transformer(nn.Module):\n def __init__(\n self, width: int, layers: int, heads: int, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU,\n drop_attention_rate: float = 0.,\n ):\n super().__init__()\n self.width = width\n self.layers = layers\n self.grad_checkpointing = False\n\n self.resblocks = nn.ModuleList([\n ResidualAttentionBlock(width, heads, mlp_ratio, act_layer=act_layer, drop_attention_rate=drop_attention_rate)\n for _ in range(layers)\n ])\n\n def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):\n for r in self.resblocks:\n if self.grad_checkpointing and not torch.jit.is_scripting():\n x = checkpoint(r, x, attn_mask)\n else:\n x = r(x, attn_mask=attn_mask)\n return x"
},
{
"path": "Quick_demo/Model/RadFM/helpers.py",
"content": "\"\"\"\nTaken from https://github.com/lucidrains/flamingo-pytorch\n\"\"\"\n\nimport torch\nfrom einops import rearrange, repeat\nfrom einops_exts import rearrange_many\nfrom torch import einsum, nn\n\n\ndef exists(val):\n return val is not None\n\n\ndef FeedForward(dim, mult=4):\n inner_dim = int(dim * mult)\n return nn.Sequential(\n nn.LayerNorm(dim),\n nn.Linear(dim, inner_dim, bias=False),\n nn.GELU(),\n nn.Linear(inner_dim, dim, bias=False),\n )\n\n\nclass PerceiverAttention(nn.Module):\n def __init__(self, *, dim, dim_head=64, heads=8):\n super().__init__()\n self.scale = dim_head**-0.5\n self.heads = heads\n inner_dim = dim_head * heads\n\n self.norm_media = nn.LayerNorm(dim)\n self.norm_latents = nn.LayerNorm(dim)\n\n self.to_q = nn.Linear(dim, inner_dim, bias=False)\n self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)\n self.to_out = nn.Linear(inner_dim, dim, bias=False)\n\n def forward(self, x, latents):\n \"\"\"\n Args:\n x (torch.Tensor): image features\n shape (b, T, n1, D)\n latent (torch.Tensor): latent features\n shape (b, T, n2, D)\n \"\"\"\n x = self.norm_media(x)\n latents = self.norm_latents(latents)\n\n h = self.heads\n\n q = self.to_q(latents)\n kv_input = torch.cat((x, latents), dim=-2)\n k, v = self.to_kv(kv_input).chunk(2, dim=-1)\n q, k, v = rearrange_many((q, k, v), \"b t n (h d) -> b h t n d\", h=h)\n q = q * self.scale\n\n # attention\n sim = einsum(\"... i d, ... j d -> ... i j\", q, k)\n sim = sim - sim.amax(dim=-1, keepdim=True).detach()\n attn = sim.softmax(dim=-1)\n\n out = einsum(\"... i j, ... j d -> ... i d\", attn, v)\n out = rearrange(out, \"b h t n d -> b t n (h d)\", h=h)\n return self.to_out(out)\n\n\nclass PerceiverResampler(nn.Module):\n def __init__(\n self,\n *,\n dim,\n depth=6,\n dim_head=64,\n heads=8,\n num_latents=64,\n max_num_media=None,\n max_num_frames=None,\n ff_mult=4,\n ):\n super().__init__()\n self.latents = nn.Parameter(torch.randn(num_latents, dim))\n self.frame_embs = (\n nn.Parameter(torch.randn(max_num_frames, dim))\n if exists(max_num_frames)\n else None\n )\n self.media_time_embs = (\n nn.Parameter(torch.randn(max_num_media, 1, dim))\n if exists(max_num_media)\n else None\n )\n\n self.layers = nn.ModuleList([])\n for _ in range(depth):\n self.layers.append(\n nn.ModuleList(\n [\n PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),\n FeedForward(dim=dim, mult=ff_mult),\n ]\n )\n )\n\n self.norm = nn.LayerNorm(dim)\n\n def forward(self, x):\n \"\"\"\n Args:\n x (torch.Tensor): image features\n shape (b, T, F, v, D)\n Returns:\n shape (b, T, n, D) where n is self.num_latents\n \"\"\"\n b, T, F, v = x.shape[:4]\n\n # frame and media time embeddings\n if exists(self.frame_embs):\n frame_embs = repeat(self.frame_embs[:F], \"F d -> b T F v d\", b=b, T=T, v=v)\n x = x + frame_embs\n x = rearrange(\n x, \"b T F v d -> b T (F v) d\"\n ) # flatten the frame and spatial dimensions\n if exists(self.media_time_embs):\n x = x + self.media_time_embs[:T]\n\n # blocks\n latents = repeat(self.latents, \"n d -> b T n d\", b=b, T=T)\n for attn, ff in self.layers:\n latents = attn(x, latents) + latents\n latents = ff(latents) + latents\n return self.norm(latents)\n\n\n# gated cross attention\n\n\nclass MaskedCrossAttention(nn.Module):\n def __init__(\n self,\n *,\n dim,\n dim_visual,\n dim_head=64,\n heads=8,\n only_attend_immediate_media=True,\n ):\n super().__init__()\n self.scale = dim_head**-0.5\n self.heads = heads\n inner_dim = dim_head * heads\n\n self.norm = nn.LayerNorm(dim)\n\n self.to_q = nn.Linear(dim, inner_dim, bias=False)\n self.to_kv = nn.Linear(dim_visual, inner_dim * 2, bias=False)\n self.to_out = nn.Linear(inner_dim, dim, bias=False)\n\n # whether for text to only attend to immediate preceding image, or all previous images\n self.only_attend_immediate_media = only_attend_immediate_media\n\n def forward(self, x, media, media_locations=None, attend_previous=True):\n \"\"\"\n Args:\n x (torch.Tensor): text features\n shape (B, T_txt, D_txt)\n media (torch.Tensor): image features\n shape (B, T_img, n, D_img) where n is the dim of the latents\n media_locations: boolean mask identifying the media tokens in x\n shape (B, T_txt)\n attend_previous: bool\n If false, ignores immediately preceding image and starts attending when following image\n \"\"\"\n _, T_img, n = media.shape[:3]\n h = self.heads\n\n x = self.norm(x)\n\n q = self.to_q(x)\n media = rearrange(media, \"b t n d -> b (t n) d\")\n\n k, v = self.to_kv(media).chunk(2, dim=-1)\n q, k, v = rearrange_many((q, k, v), \"b n (h d) -> b h n d\", h=h)\n\n q = q * self.scale\n\n sim = einsum(\"... i d, ... j d -> ... i j\", q, k)\n\n if exists(media_locations):\n # at each boolean of True, increment the time counter (relative to media time)\n text_time = media_locations.cumsum(dim=-1)\n media_time = torch.arange(T_img, device=x.device) + 1\n\n if not attend_previous:\n text_time[~media_locations] += 1\n # make sure max is still the number of images in the sequence\n text_time[\n text_time\n > repeat(\n torch.count_nonzero(media_locations, dim=1),\n \"b -> b i\",\n i=text_time.shape[1],\n )\n ] = 0\n\n # text time must equal media time if only attending to most immediate image\n # otherwise, as long as text time is greater than media time (if attending to all previous images / media)\n mask_op = torch.eq if self.only_attend_immediate_media else torch.ge\n\n text_to_media_mask = mask_op(\n rearrange(text_time, \"b i -> b 1 i 1\"),\n repeat(media_time, \"j -> 1 1 1 (j n)\", n=n),\n )\n sim = sim.masked_fill(~text_to_media_mask, -torch.finfo(sim.dtype).max)\n\n sim = sim - sim.amax(dim=-1, keepdim=True).detach()\n attn = sim.softmax(dim=-1)\n\n if exists(media_locations) and self.only_attend_immediate_media:\n # any text without a preceding media needs to have attention zeroed out\n text_without_media_mask = text_time == 0\n text_without_media_mask = rearrange(\n text_without_media_mask, \"b i -> b 1 i 1\"\n )\n attn = attn.masked_fill(text_without_media_mask, 0.0)\n\n out = einsum(\"... i j, ... j d -> ... i d\", attn, v)\n out = rearrange(out, \"b h n d -> b n (h d)\")\n return self.to_out(out)\n\n\nclass GatedCrossAttentionBlock(nn.Module):\n def __init__(\n self,\n *,\n dim,\n dim_visual,\n dim_head=64,\n heads=8,\n ff_mult=4,\n only_attend_immediate_media=True,\n ):\n super().__init__()\n self.attn = MaskedCrossAttention(\n dim=dim,\n dim_visual=dim_visual,\n dim_head=dim_head,\n heads=heads,\n only_attend_immediate_media=only_attend_immediate_media,\n )\n self.attn_gate = nn.Parameter(torch.tensor([0.0]))\n\n self.ff = FeedForward(dim, mult=ff_mult)\n self.ff_gate = nn.Parameter(torch.tensor([0.0]))\n\n def forward(\n self,\n x,\n media,\n media_locations=None,\n attend_previous=True,\n ):\n x = (\n self.attn(\n x,\n media,\n media_locations=media_locations,\n attend_previous=attend_previous,\n )\n * self.attn_gate.tanh()\n + x\n )\n x = self.ff(x) * self.ff_gate.tanh() + x\n\n return x\n"
},
{
"path": "Quick_demo/Model/RadFM/multimodality_model.py",
"content": "# Import necessary libraries\nfrom torch import nn\nfrom transformers.models.llama import LlamaForCausalLM\nfrom .my_embedding_layer import MyEmbedding\nfrom torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\nimport tqdm.auto as tqdm\nimport torch.nn as nn\nimport torch\nfrom torch.utils.checkpoint import checkpoint\nfrom torch.autograd import Variable\nimport numpy as np\n\nclass MultiLLaMAForCausalLM(nn.Module):\n \"\"\"\n A multimodal LLaMA model that combines language and vision inputs\n for causal language modeling tasks.\n \"\"\"\n def __init__(self, lang_model_path): \n \"\"\"\n Initialize the multimodal model.\n \n Args:\n lang_model_path (str): Path to the pretrained language model\n \"\"\"\n super(MultiLLaMAForCausalLM, self).__init__() \n \n # Load pretrained LLaMA model\n self.lang_model = LlamaForCausalLM.from_pretrained(\n lang_model_path,\n )\n \n # Enable gradient checkpointing for memory efficiency\n self.lang_model.gradient_checkpointing_enable()\n self.lang_model.enable_input_require_grads()\n \n # Initialize custom embedding layer and share weights with language model\n self.embedding_layer = MyEmbedding()\n self.embedding_layer.weight = self.lang_model.get_input_embeddings().weight\n \n # Set model dimensions\n self.hidden_dim = 5120\n self.voc_size = 32000\n \n def forward(self, lang_x, vision_x, attention_mask, labels, loss_reweight, key_words_query):\n \"\"\"\n Forward pass for the multimodal model.\n \n Args:\n lang_x: Language input tokens\n vision_x: Vision input features\n attention_mask: Attention mask for language inputs\n labels: Target labels for language modeling\n loss_reweight: Weights for calculating loss (to prioritize certain tokens)\n key_words_query: Query for highlighting important words\n \n Returns:\n Dictionary containing model outputs including loss and logits\n \"\"\"\n if labels.shape == lang_x.shape:\n # Set embedding mode to handle text inputs\n self.embedding_layer.flag = 'Text'\n \n # Get embeddings and matching loss from embedding layer\n input_embedding, loss_match = self.embedding_layer(lang_x, vision_x, key_words_query)\n \n # Forward pass through the language model\n output = self.lang_model(inputs_embeds=input_embedding, attention_mask=attention_mask, labels=labels)\n logits = output['logits']\n\n # Initialize regularization loss\n loss_reg = None\n if labels is not None:\n # Shift logits and labels for next-token prediction\n shift_logits = logits[..., :-1, :].contiguous()\n shift_labels = labels[..., 1:].contiguous()\n shift_loss_reweight = loss_reweight[..., 1:].contiguous()\n \n # Prepare for loss calculation\n loss_fct = CrossEntropyLoss(reduction='none')\n shift_logits = shift_logits.view(-1, self.voc_size)\n shift_labels = shift_labels.view(-1)\n shift_loss_reweight = shift_loss_reweight.view(-1)\n \n # Ensure tensors are on the same device\n shift_labels = shift_labels.to(shift_logits.device)\n shift_loss_reweight = shift_loss_reweight.to(shift_logits.device) \n \n # Calculate weighted cross-entropy loss\n loss_reg = loss_fct(shift_logits, shift_labels)\n loss_reg = torch.sum(shift_loss_reweight * loss_reg) / torch.sum(shift_loss_reweight)\n \n # Combine losses\n loss = loss_reg\n if loss_match is not None:\n loss = 0.8 * loss + 0.2 * loss_match\n \n # Calculate accuracy metrics\n logits = output['logits'][..., :-1, :].contiguous().detach()\n total = len(labels)\n predictions = torch.argmax(logits, dim=-1)\n labels = labels[..., 1:].contiguous()\n \n # Count correct predictions (ignoring padding tokens with -100)\n Acc = torch.sum(torch.all(torch.logical_or(predictions == labels, labels == -100), dim=-1)) \n Accuracy = Acc / total \n \n return dict(\n # loss_reg = loss_reg,\n # loss_matching = loss_matching,\n logits=Accuracy,\n loss=output['loss'],\n )\n \n ### useless for now ignore the folowing codes ###\n # if labels.shape == vision_x.shape:\n # self.embedding_layer.flag = 'Seg'\n # input_embedding = self.embedding_layer(lang_x, vision_x)\n \n def generate(self, lang_x, vision_x):\n \"\"\"\n Generate text based on language and vision inputs.\n \n Args:\n lang_x: Language input tokens\n vision_x: Vision input features\n \n Returns:\n Generated token sequence\n \"\"\"\n # Set embedding mode to text generation\n self.embedding_layer.flag = 'Text'\n \n with torch.no_grad():\n # Get embeddings from the embedding layer\n input_embedding, _ = self.embedding_layer(lang_x, vision_x) \n \n # Generate text using language model\n generation = self.lang_model.generate(\n inputs_embeds=input_embedding, \n max_new_tokens=200,\n top_k=50\n )\n \n return generation"
},
{
"path": "Quick_demo/Model/RadFM/my_embedding_layer.py",
"content": "# Import necessary libraries\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch \nfrom .helpers import PerceiverResampler \nfrom .utils import get_visual_encoder\nfrom einops import rearrange, repeat\nfrom einops_exts import rearrange_many\nimport torchvision\nfrom .vit_3d import ViT\nfrom einops.layers.torch import Rearrange\nfrom .transformer_decoder import TransformerDecoder, TransformerDecoderLayer\nfrom torch.utils.checkpoint import checkpoint\nfrom torch.autograd import Variable\nimport random\nfrom transformers import AutoTokenizer, AutoModel\n\nclass MyEmbedding(nn.Module):\n \"\"\"\n Custom embedding layer for multimodal inputs that combines text and vision features.\n \"\"\"\n def __init__(self, num_embeddings=32000, embedding_dim=5120, perceiver_num=32, vis_dim=768, \n patch_size=32, frame_patch_size=4, seg_channel=256):\n \"\"\"\n Initialize the multimodal embedding layer.\n \n Args:\n num_embeddings (int): Size of vocabulary for text embeddings\n embedding_dim (int): Dimension of output embeddings\n perceiver_num (int): Number of latent vectors in perceiver\n vis_dim (int): Dimension of vision features\n patch_size (int): Size of image patches\n frame_patch_size (int): Size of 3D frame patches\n seg_channel (int): Number of segmentation channels\n \"\"\"\n super().__init__()\n self.num_embeddings = num_embeddings\n self.embedding_dim = embedding_dim\n # Standard embedding weight matrix for text tokens\n self.weight = nn.Parameter(torch.torch.randn((num_embeddings, embedding_dim)))\n # Special token weights for figures/images\n self.figure_token_weight = nn.Parameter(torch.randn((2, embedding_dim)))\n self.flag = 'Text' # Mode flag: 'Text' or 'Seg'\n self.patch_size = patch_size \n self.frame_patch_size = frame_patch_size\n self.seg_channel = seg_channel\n \n ## the MedKEBERT can be downloaded from https://huggingface.co/xmcmic/Med-KEBERT/tree/main ##\n # Initialize medical domain BERT model for keyword understanding\n self.bert_tokenizer = AutoTokenizer.from_pretrained(\"xmcmic/Med-KEBERT\")\n self.bert_model = AutoModel.from_pretrained(\"xmcmic/Med-KEBERT\")\n # Project BERT outputs to vision feature space\n self.bert_projection_fc = nn.Linear(768, vis_dim)\n \n # 3D Vision Transformer for processing volumetric medical images\n self.vision_encoder = ViT(\n image_size=512, # image size\n frames=512, # max number of frames\n image_patch_size=patch_size, # image patch size\n frame_patch_size=frame_patch_size, # frame patch size\n dim=vis_dim,\n depth=12,\n heads=8,\n mlp_dim=2048,\n dropout=0.1,\n emb_dropout=0.1\n )\n \n # Upscaling layers for vision features (used in segmentation mode)\n self.output_upscaling = nn.Sequential(\n nn.ConvTranspose3d(vis_dim, vis_dim // 4, kernel_size=2, stride=2),\n nn.BatchNorm3d(vis_dim // 4),\n nn.GELU(),\n nn.ConvTranspose3d(vis_dim // 4, vis_dim // 8, kernel_size=2, stride=2),\n nn.GELU(),\n )\n \n # Transformer decoder for cross-attention between text and vision\n decoder_layer = TransformerDecoderLayer(d_model=vis_dim, nhead=8, normalize_before=True)\n decoder_norm = nn.LayerNorm(vis_dim)\n self.transformer_decoder = TransformerDecoder(decoder_layer=decoder_layer, num_layers=4, norm=decoder_norm)\n \n # MLP for processing transformer decoder outputs\n self.transformer_decoder_mlp = nn.Sequential(\n nn.Linear(vis_dim, vis_dim // 4),\n nn.GELU(),\n nn.Linear(vis_dim // 4, vis_dim // 8),\n nn.GELU(),\n )\n self.vis_dim = vis_dim\n \n # Perceiver resampler to reduce sequence length of vision features\n self.perceiver = PerceiverResampler(dim=self.vis_dim, num_latents=perceiver_num)\n # Final projection to embedding dimension\n self.fc = nn.Linear(self.vis_dim, self.embedding_dim)\n # Classification head for matching keywords\n self.cls_head = nn.Linear(self.vis_dim // 8, 1)\n \n\n def forward(self, text_input, vision_x, key_words_query=None):\n \"\"\"\n Forward pass for the embedding layer.\n \n Args:\n text_input: Text token indices [B, L]\n vision_x: Visual input features [B, S, C, H, W, D]\n key_words_query: Optional list of key words for contrastive learning\n \n Returns:\n tuple: (output_embeddings, loss_matching)\n - output_embeddings: Combined embeddings for text and vision\n - loss_matching: Contrastive loss for keyword matching (or None)\n \"\"\"\n if self.flag == 'Text':\n # Process in text mode\n B, S, C, H, W, D = vision_x.shape\n # Reshape for batch processing\n vision_x = rearrange(vision_x, \"b S c h w d-> (b S) c h w d\")\n \n # Process through vision encoder\n vision_x, pos_embedding = self.vision_encoder(vision_x)\n \n # Reshape back to batch format\n vision_x = rearrange(vision_x, \"(b s F) v d -> b s F v d\", b=B, s=S, F=1) \n \n loss_matching = None\n \n if key_words_query is not None:\n ## we do not use the following parts in final version. \n ## You can quota the following codes and if so the bert models will be useless.\n # key_words_query list[list[str]] B, words, each word matches corresponding vision_x embedding\n \n # Extract and deduplicate keywords\n query_words = [item for sublist in key_words_query for item in sublist]\n query_words = list(set(query_words))\n \n # Limit number of keywords to process\n if len(query_words) > 16:\n random.shuffle(query_words)\n query_words = query_words[0:16]\n \n if query_words != []:\n # Create binary labels for contrastive learning\n contrastive_labels = torch.zeros(B, len(query_words)) # B Q\n for i, sublist in enumerate(key_words_query):\n for j, item in enumerate(query_words):\n if item in sublist:\n contrastive_labels[i, j] = 1 \n contrastive_labels = contrastive_labels.to(vision_x.dtype).to(vision_x.device) \n \n # Get BERT embeddings for keywords\n with torch.no_grad():\n query_words_embedding = self.bert_tokenizer(\n query_words, \n padding='max_length', \n truncation=True, \n max_length=256,\n return_tensors=\"pt\"\n )\n query_words_embedding = self.bert_model(\n input_ids=query_words_embedding['input_ids'].to(vision_x.device),\n attention_mask=query_words_embedding['attention_mask'].to(vision_x.device)\n )['last_hidden_state'][:, 0, :].to(vision_x.dtype).to(vision_x.device) # Q,D\n \n # Project BERT embeddings to vision space\n query_words_embedding = self.bert_projection_fc(query_words_embedding)\n query_words_embedding = query_words_embedding.unsqueeze(0).repeat(B, 1, 1) # B,Q,D\n _, N, _ = query_words_embedding.shape\n \n # Pool vision features\n image_embedding = vision_x.mean(dim=1) # B V D average pooling to remove multimodality\n image_embedding = rearrange(image_embedding, \"b F v d -> b (F v) d\")\n pos_embedding = rearrange(pos_embedding, \"(b s) v d -> b s v d\", b=B, s=S)[:, 0, :, :]\n \n # Prepare inputs for transformer decoder\n image_embedding = image_embedding.transpose(0, 1) # (H/P W/P D/P) B D\n pos_embedding = pos_embedding.transpose(0, 1) # (H/P W/P D/P) B D\n query_words_embedding = query_words_embedding.transpose(0, 1) # N B D\n \n # Cross-attention between keywords and image features\n oo_embedding, _ = self.transformer_decoder(\n query_words_embedding, image_embedding, pos=pos_embedding\n ) \n oo_embedding = oo_embedding.transpose(0, 1) # B Q D\n oo_embedding = rearrange(oo_embedding, 'b n d -> (b n) d')\n oo_embedding = self.transformer_decoder_mlp(oo_embedding)\n oo_embedding = self.cls_head(oo_embedding).mean(dim=-1)\n oo_embedding = rearrange(oo_embedding, '(b n) -> b n', b=B, n=N) # B Q \n \n # Calculate contrastive loss\n loss_matching = F.binary_cross_entropy_with_logits(oo_embedding, contrastive_labels) \n \n # Process vision features through perceiver resampler\n vision_x = self.perceiver(vision_x) # reshapes to (b, S, n, d)\n \n n = vision_x.shape[2]\n \n # Project vision features to embedding dimension\n vision_x = rearrange(vision_x, \"b s n d -> (b s n) d\")\n vision_x = self.fc(vision_x)\n vision_x = rearrange(vision_x, \"(b T) d -> b T d\", b=B, T=n*S)\n \n # Combine text and vision embeddings\n embedding_weight = torch.cat([self.weight, self.figure_token_weight], dim=0)\n embedding_weight = embedding_weight.unsqueeze(0).repeat(B, 1, 1)\n embedding_weight = torch.cat([embedding_weight, vision_x], dim=1)\n \n # Convert text indices to one-hot and compute final embeddings\n text_input = F.one_hot(text_input, embedding_weight.shape[1]).to(vision_x.dtype).to(vision_x.device)\n out_put = torch.matmul(text_input, embedding_weight)\n \n ## useless for now. ignore the folowing code## \n # if self.flag == 'Seg':\n # B,C,H,W,D = vision_x.shape\n # _,N,_ = text_input.shape\n # latent_embedding, pos_embedding = self.vision_encoder(vision_x) # B (H/P W/P D/P) D\n \n # image_embedding = latent_embedding.transpose(0,1) # (H/P W/P D/P) B D\n # pos_embedding = pos_embedding.transpose(0,1) # (H/P W/P D/P) B D\n # text_input = text_input.transpose(0,1) # N B D\n \n # mask_embedding,_ = self.transformer_decoder(text_input, image_embedding, pos = pos_embedding) \n # mask_embedding = mask_embedding.transpose(0,1) # B N D\n # mask_embedding = rearrange(mask_embedding, 'b n d -> (b n) d')\n # mask_embedding = self.transformer_decoder_mlp(mask_embedding)\n # mask_embedding = rearrange(mask_embedding, '(b n) d -> b n d', b=B, n=N,d = self.vis_dim // 8)\n \n # vision_x = rearrange(latent_embedding,'b (h w d) c -> b c h w d', h = (H // self.patch_size), w = (W // self.patch_size), d = (D // self.frame_patch_size), c=self.vis_dim)\n # vision_x = self.output_upscaling(vision_x) #B C H/4 W/4 D/4\n # out_put = torch.einsum('bchwd,bnc->bnhwd', vision_x, mask_embedding)\n \n return out_put, loss_matching\n\n# model = MyEmbedding(vision_encoder_path = '')\n# text_input = torch.randint(low=0, high=3210, size=(4,2048))\n# image_input = torch.randn((4,3,3,512,512,4))\n# key_words_query = [[],[],[],['consoliation']]\n# print(model(text_input, image_input, key_words_query))"
},
{
"path": "Quick_demo/Model/RadFM/position_encoding.py",
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nVarious positional encodings for the transformer.\n\"\"\"\nimport math\nimport torch\nfrom torch import nn\nfrom einops.layers.torch import Rearrange\nfrom einops import rearrange, repeat\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention is all you need paper, generalized to work on images.\n \"\"\"\n def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):\n super().__init__()\n self.num_pos_feats = num_pos_feats\n self.temperature = temperature\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n def forward(self, tensor_list):\n x = tensor_list.tensors\n mask = tensor_list.mask\n assert mask is not None\n not_mask = ~mask\n y_embed = not_mask.cumsum(1, dtype=torch.float32)\n x_embed = not_mask.cumsum(2, dtype=torch.float32)\n if self.normalize:\n eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)\n pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n return pos\n\n\nclass PositionEmbeddingLearned(nn.Module):\n \"\"\"\n Absolute pos embedding, learned.\n \"\"\"\n def __init__(self, num_pos_feats=256):\n super().__init__()\n self.row_embed = nn.Embedding(50, num_pos_feats)\n self.col_embed = nn.Embedding(50, num_pos_feats)\n self.reset_parameters()\n\n def reset_parameters(self):\n nn.init.uniform_(self.row_embed.weight)\n nn.init.uniform_(self.col_embed.weight)\n\n def forward(self, tensor_list):\n x = tensor_list.tensors\n h, w = x.shape[-2:]\n i = torch.arange(w, device=x.device)\n j = torch.arange(h, device=x.device)\n x_emb = self.col_embed(i)\n y_emb = self.row_embed(j)\n pos = torch.cat([\n x_emb.unsqueeze(0).repeat(h, 1, 1),\n y_emb.unsqueeze(1).repeat(1, w, 1),\n ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1)\n return pos\n\nclass PositionEmbeddingLearned3d(nn.Module):\n \"\"\"\n Absolute pos embedding, learned.\n \"\"\"\n def __init__(self, num_pos_feats=256,h_patch_num = 16, w_patch_num = 16,d_patch_num = 64):\n super().__init__()\n self.h_patch_num = h_patch_num\n self.w_patch_num = w_patch_num\n self.d_patch_num = d_patch_num\n self.row_embed = nn.Embedding(h_patch_num, num_pos_feats)\n self.col_embed = nn.Embedding(w_patch_num, num_pos_feats)\n self.dep_embed = nn.Embedding(d_patch_num, num_pos_feats)\n self.reset_parameters()\n\n def reset_parameters(self):\n nn.init.uniform_(self.row_embed.weight)\n nn.init.uniform_(self.col_embed.weight)\n nn.init.uniform_(self.dep_embed.weight)\n\n def forward(self, B, h, w, d,x):\n i = (torch.arange(h, device=x.device) + 1)* (self.h_patch_num // h) -1\n j = (torch.arange(w, device=x.device) + 1)* (self.w_patch_num // w) -1\n k = (torch.arange(d, device=x.device) + 1)* (self.d_patch_num // d) -1\n x_emb = self.row_embed(i).unsqueeze(1).unsqueeze(2).repeat(1,w,d,1)\n y_emb = self.col_embed(j).unsqueeze(0).unsqueeze(2).repeat(h,1,d,1)\n z_emb = self.dep_embed(k).unsqueeze(0).unsqueeze(1).repeat(h,w,1,1)\n pos = torch.cat([x_emb,y_emb,z_emb,], dim=-1).unsqueeze(0).repeat(B, 1, 1, 1, 1)\n pos = rearrange(pos,'b h w d c -> b (h w d) c')\n return pos\n \ndef build_position_encoding(args):\n N_steps = args.hidden_dim // 2\n if args.position_embedding in ('v2', 'sine'):\n # TODO find a better way of exposing other arguments\n position_embedding = PositionEmbeddingSine(N_steps, normalize=True)\n elif args.position_embedding in ('v3', 'learned'):\n position_embedding = PositionEmbeddingLearned(N_steps)\n else:\n raise ValueError(f\"not supported {args.position_embedding}\")\n\n return position_embedding\n\n# Pos = PositionEmbeddingLearned3d()\n# x = torch.randn((8,3,32,32,1))\n# print(Pos(8,16,16,1,x))"
},
{
"path": "Quick_demo/Model/RadFM/transformer_decoder.py",
"content": "\"\"\"\nCode modified from DETR tranformer:\nhttps://github.com/facebookresearch/detr\nCopyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\n\nimport copy\nfrom typing import Optional, List\nimport pickle as cp\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\n\n\nclass TransformerDecoder(nn.Module):\n\n def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):\n super().__init__()\n self.layers = _get_clones(decoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n self.return_intermediate = return_intermediate\n\n def forward(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n output = tgt\n T,B,C = memory.shape\n intermediate = []\n atten_layers = []\n for n,layer in enumerate(self.layers):\n \n residual=True\n output,ws = layer(output, memory, tgt_mask=tgt_mask,\n memory_mask=memory_mask,\n tgt_key_padding_mask=tgt_key_padding_mask,\n memory_key_padding_mask=memory_key_padding_mask,\n pos=pos, query_pos=query_pos,residual=residual)\n atten_layers.append(ws)\n if self.return_intermediate:\n intermediate.append(self.norm(output))\n if self.norm is not None:\n output = self.norm(output)\n if self.return_intermediate:\n intermediate.pop()\n intermediate.append(output)\n\n if self.return_intermediate:\n return torch.stack(intermediate)\n return output,atten_layers\n\n\n\nclass TransformerDecoderLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.dropout3 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n residual=True):\n q = k = self.with_pos_embed(tgt, query_pos)\n tgt2,ws = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)\n tgt = self.norm1(tgt)\n tgt2,ws = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)\n\n\n # attn_weights [B,NUM_Q,T]\n tgt = tgt + self.dropout2(tgt2)\n tgt = self.norm2(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout3(tgt2)\n tgt = self.norm3(tgt)\n return tgt,ws\n\n def forward_pre(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n tgt2 = self.norm1(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2,ws = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)\n tgt = tgt + self.dropout1(tgt2)\n tgt2 = self.norm2(tgt)\n tgt2,attn_weights = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)\n tgt = tgt + self.dropout2(tgt2)\n tgt2 = self.norm3(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout3(tgt2)\n return tgt,attn_weights\n\n def forward(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n residual=True):\n if self.normalize_before:\n return self.forward_pre(tgt, memory, tgt_mask, memory_mask,\n tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)\n return self.forward_post(tgt, memory, tgt_mask, memory_mask,\n tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos,residual)\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n"
},
{
"path": "Quick_demo/Model/RadFM/utils.py",
"content": "from .blocks import ModifiedResNet,PMC_CLIP_cfg\nimport torch\nfrom torchvision import transforms\nfrom PIL import Image\nimport torch.nn as nn\ndef extend_instance(obj, mixin):\n \"\"\"Apply mixins to a class instance after creation\"\"\"\n base_cls = obj.__class__\n base_cls_name = obj.__class__.__name__\n obj.__class__ = type(\n base_cls_name, (mixin, base_cls), {}\n ) # mixin needs to go first for our forward() logic to work\n\n\ndef getattr_recursive(obj, att):\n \"\"\"\n Return nested attribute of obj\n Example: getattr_recursive(obj, 'a.b.c') is equivalent to obj.a.b.c\n \"\"\"\n if att == \"\":\n return obj\n i = att.find(\".\")\n if i < 0:\n return getattr(obj, att)\n else:\n return getattr_recursive(getattr(obj, att[:i]), att[i + 1 :])\n\n\ndef setattr_recursive(obj, att, val):\n \"\"\"\n Set nested attribute of obj\n Example: setattr_recursive(obj, 'a.b.c', val) is equivalent to obj.a.b.c = val\n \"\"\"\n if \".\" in att:\n obj = getattr_recursive(obj, \".\".join(att.split(\".\")[:-1]))\n setattr(obj, att.split(\".\")[-1], val)\n\n\n \ndef get_visual_encoder(model_str):\n \"\"\"\n Args:\n str (_type_): str_to_model_path\n Return:\n vision_model, visual_dim, img_preprocessor\n \"\"\"\n normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n img_preprocessor = transforms.Compose([ \n transforms.Resize((512,512), interpolation=Image.BICUBIC),\n transforms.ToTensor(),\n normalize,\n ])\n if 'PMC-CLIP' in model_str:\n #vision_cfg = json.load(open(model_args.visual_model_config,'r'))['vision_cfg']\n vision_cfg = PMC_CLIP_cfg()\n vision_heads = vision_cfg.width * 32 // vision_cfg.head_width\n vision_model = ModifiedResNet(\n layers=vision_cfg.layers,\n heads=vision_heads,\n output_dim = 768,\n image_size=vision_cfg.image_size,\n width=vision_cfg.width\n )\n vision_model = vision_load_pretrain(vision_model,model_str)\n vision_model = nn.Sequential(*list(vision_model.children())[:-2])\n visual_dim = 1024\n return vision_model,visual_dim,img_preprocessor\n\ndef vision_load_pretrain(resnet,model_path):\n checkpoint = torch.load(model_path, map_location='cpu') \n state_dict = checkpoint['state_dict'] \n state_dict = {k.replace('module.visual.',''): v for k, v in state_dict.items() if '.visual' in k}\n resnet.load_state_dict(state_dict)\n return resnet \n"
},
{
"path": "Quick_demo/Model/RadFM/vit_3d.py",
"content": "import torch\nfrom torch import nn\n\nfrom einops import rearrange, repeat\nfrom einops.layers.torch import Rearrange\nfrom .position_encoding import PositionEmbeddingLearned3d\n\n# helpers\n\ndef pair(t):\n return t if isinstance(t, tuple) else (t, t)\n\n# classes\n\nclass PreNorm(nn.Module):\n def __init__(self, dim, fn):\n super().__init__()\n self.norm = nn.LayerNorm(dim)\n self.fn = fn\n def forward(self, x, **kwargs):\n return self.fn(self.norm(x), **kwargs)\n\nclass FeedForward(nn.Module):\n def __init__(self, dim, hidden_dim, dropout = 0.):\n super().__init__()\n self.net = nn.Sequential(\n nn.Linear(dim, hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, dim),\n nn.Dropout(dropout)\n )\n def forward(self, x):\n return self.net(x)\n\nclass Attention(nn.Module):\n def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):\n super().__init__()\n inner_dim = dim_head * heads\n project_out = not (heads == 1 and dim_head == dim)\n\n self.heads = heads\n self.scale = dim_head ** -0.5\n\n self.attend = nn.Softmax(dim = -1)\n self.dropout = nn.Dropout(dropout)\n\n self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)\n\n self.to_out = nn.Sequential(\n nn.Linear(inner_dim, dim),\n nn.Dropout(dropout)\n ) if project_out else nn.Identity()\n\n def forward(self, x):\n qkv = self.to_qkv(x).chunk(3, dim = -1)\n q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)\n\n dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale\n\n attn = self.attend(dots)\n attn = self.dropout(attn)\n\n out = torch.matmul(attn, v)\n out = rearrange(out, 'b h n d -> b n (h d)')\n return self.to_out(out)\n\nclass Transformer(nn.Module):\n def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):\n super().__init__()\n self.layers = nn.ModuleList([])\n for _ in range(depth):\n self.layers.append(nn.ModuleList([\n PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),\n PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))\n ]))\n def forward(self, x):\n for attn, ff in self.layers:\n x = attn(x) + x\n x = ff(x) + x\n return x\n\nclass ViT(nn.Module):\n def __init__(self, *, image_size, image_patch_size, frames, frame_patch_size, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):\n super().__init__()\n image_height, image_width = pair(image_size)\n patch_height, patch_width = pair(image_patch_size)\n\n assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'\n assert frames % frame_patch_size == 0, 'Frames must be divisible by frame patch size'\n\n self.patch_height = patch_height\n self.patch_width = patch_width\n self.frame_patch_size = frame_patch_size\n \n num_patches = (image_height // patch_height) * (image_width // patch_width) * (frames // frame_patch_size)\n patch_dim = channels * patch_height * patch_width * frame_patch_size\n\n assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'\n \n self.to_patch_embedding = nn.Sequential(\n Rearrange('b c (h p1) (w p2) (f pf) -> b (h w f) (p1 p2 pf c)', p1 = patch_height, p2 = patch_width, pf = frame_patch_size),\n nn.LayerNorm(patch_dim),\n nn.Linear(patch_dim, dim),\n nn.LayerNorm(dim),\n )\n\n self.pos_embedding = PositionEmbeddingLearned3d(dim // 3,(image_height // patch_height), (image_width // patch_width), (frames // frame_patch_size))\n self.dropout = nn.Dropout(emb_dropout)\n\n self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)\n\n def forward(self, video):\n B, C, H, W, D = video.shape\n x = self.to_patch_embedding(video)\n b, n, _ = x.shape\n\n pos = self.pos_embedding(B, H // self.patch_height, W // self.patch_width, D // self.frame_patch_size,x)\n x += pos\n x = self.dropout(x)\n\n x = self.transformer(x)\n return x,pos\n"
},
{
"path": "Quick_demo/test.py",
"content": "# Import necessary libraries for data processing, model loading, and inference\nimport tqdm.auto as tqdm\nimport torch.nn.functional as F\nfrom typing import Optional, Dict, Sequence\nfrom typing import List, Optional, Tuple, Union\nimport transformers\nfrom dataclasses import dataclass, field\nfrom Model.RadFM.multimodality_model import MultiLLaMAForCausalLM\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer\nfrom torchvision import transforms\nfrom PIL import Image \n\ndef get_tokenizer(tokenizer_path, max_img_size=100, image_num=32):\n '''\n Initialize the tokenizer with special tokens for image handling\n \n Args:\n tokenizer_path: Path to the base tokenizer\n max_img_size: Maximum number of images supported in a prompt\n image_num: Number of token embeddings per image\n \n Returns:\n Tuple of (tokenizer, image_padding_tokens)\n '''\n if isinstance(tokenizer_path, str):\n image_padding_tokens = []\n # Load the base tokenizer from the provided path\n text_tokenizer = LlamaTokenizer.from_pretrained(\n tokenizer_path,\n )\n # Define initial special tokens for image markup\n special_token = {\"additional_special_tokens\": [\"\", \"\"]}\n \n # Generate unique tokens for each image position and patch\n for i in range(max_img_size):\n image_padding_token = \"\"\n \n for j in range(image_num):\n image_token = \"\"\n image_padding_token = image_padding_token + image_token\n special_token[\"additional_special_tokens\"].append(\"\")\n \n # Store the concatenated tokens for each image\n image_padding_tokens.append(image_padding_token)\n \n # Add all special tokens to the tokenizer\n text_tokenizer.add_special_tokens(\n special_token\n )\n \n # Configure standard special tokens for LLaMA models\n text_tokenizer.pad_token_id = 0\n text_tokenizer.bos_token_id = 1\n text_tokenizer.eos_token_id = 2 \n \n return text_tokenizer, image_padding_tokens \n\ndef combine_and_preprocess(question, image_list, image_padding_tokens):\n '''\n Combine text and images into a multimodal input format\n \n Args:\n question: Text input or question to process\n image_list: List of images with their metadata\n image_padding_tokens: Special tokens for image placeholders\n \n Returns:\n Tuple of (processed_text, processed_images_tensor)\n '''\n # Define image transformation pipeline\n transform = transforms.Compose([ \n transforms.RandomResizedCrop([512, 512], scale=(0.8, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n transforms.ToTensor(),\n ])\n \n images = []\n new_qestions = [_ for _ in question] # Convert question string to list of characters\n padding_index = 0\n \n # Process each image in the list\n for img in image_list:\n img_path = img['img_path']\n position = img['position'] # Where to insert the image in the text\n \n # Load and transform the image\n image = Image.open(img_path).convert('RGB') \n image = transform(image)\n image = image.unsqueeze(0).unsqueeze(-1) # Add batch and depth dimensions (c,w,h,d)\n \n # Resize the image to target dimensions\n target_H = 512 \n target_W = 512 \n target_D = 4 \n # This can be different for 3D and 2D images. For demonstration we here set this as the default sizes for 2D images. \n images.append(torch.nn.functional.interpolate(image, size=(target_H, target_W, target_D)))\n \n # Insert image placeholder token at the specified position in text\n new_qestions[position] = \"\" + image_padding_tokens[padding_index] + \"\" + new_qestions[position]\n padding_index += 1\n \n # Stack all images into a batch and add batch dimension\n vision_x = torch.cat(images, dim=1).unsqueeze(0) # Cat tensors and expand the batch_size dim\n \n # Join the character list back into a string\n text = ''.join(new_qestions) \n return text, vision_x\n \n \ndef main():\n '''\n Main function to demonstrate the RadFM model inference\n '''\n print(\"Setup tokenizer\")\n # Initialize tokenizer with special image tokens\n text_tokenizer, image_padding_tokens = get_tokenizer('./Language_files')\n print(\"Finish loading tokenizer\")\n \n ### Initialize a simple case for demo ###\n print(\"Setup demo case\")\n # Define a medical question about a chest X-ray\n question = \"Can you identify any visible signs of Cardiomegaly in the image?\"\n \n # Specify the image path and where to insert it in the question\n image = [\n {\n 'img_path': './view1_frontal.jpg',\n 'position': 0, # Insert at the beginning of the question\n }, # Can add arbitrary number of images\n ] \n \n # Combine text and images into model-ready format\n text, vision_x = combine_and_preprocess(question, image, image_padding_tokens) \n print(\"Finish loading demo case\")\n \n print(\"Setup Model\")\n # Initialize the multimodal model\n model = MultiLLaMAForCausalLM(\n lang_model_path='./Language_files', # Build up model based on LLaMa-13B config\n )\n \n # Load pretrained model weights\n ckpt = torch.load('./pytorch_model.bin', map_location='cpu') # Please download our checkpoint from huggingface and decompress the original zip file first\n model.load_state_dict(ckpt)\n print(\"Finish loading model\")\n \n # Move model to GPU and set to evaluation mode\n model = model.to('cuda')\n model.eval() \n \n # Run inference without gradient computation\n with torch.no_grad():\n # Tokenize the combined text with image placeholders\n lang_x = text_tokenizer(\n text, max_length=2048, truncation=True, return_tensors=\"pt\"\n )['input_ids'].to('cuda')\n \n # Move image tensor to GPU\n vision_x = vision_x.to('cuda')\n \n # Generate text response\n generation = model.generate(lang_x, vision_x)\n \n # Decode the generated token IDs to text\n generated_texts = text_tokenizer.batch_decode(generation, skip_special_tokens=True) \n \n # Print results\n print('---------------------------------------------------')\n print('Input: ', question)\n print('Output: ', generated_texts[0])\n\n \nif __name__ == \"__main__\":\n main()"
},
{
"path": "README.md",
"content": "# RadFM\nThe official code for the paper \"Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data\"\n\n[ArXiv](http://arxiv.org/abs/2308.02463)\n\n[Website](https://chaoyi-wu.github.io/RadFM/)\n\n[Model checkpoint](https://huggingface.co/chaoyi-wu/RadFM)\n\nIn this project, we collect a large-scale medical multi-modal dataset, MedMD, with **16M** 2D or **3D** images. We train a new medical multi-modal generative model RadFM on it, enabling both **2D and 3D** scans, multi-image input and visual-language interleaving cases.\n\n![](\"https://github.com/chaoyi-wu/RadFM/blob/main/Images/GIF.gif\"/)
\n\n## Latest News：\nAll Datasets are released! We have updated the links in [our dataset table](#dataset-links). You can find all our text part data in https://huggingface.co/datasets/chaoyi-wu/RadFM_data_csv. \n\nFor decompressing the splited compression files in most cases, please check the following code in linux:\n```\ncat zip.z* > myzip.zip\nunzip myzip.zip\n```\n\n## Quick Start:\n\nFor quick start, you can check the `Quick_demo` path. \nWe demonstrate a simple diagnosis case here to show how to inference with our model. \nFeel free to modify it as you want.\n\n- S1. Download [Model checkpoint](https://huggingface.co/chaoyi-wu/RadFM) or form [baiduyun](https://pan.baidu.com/s/1A-K5nXCbvWAVqvb6dLjYJg?pwd=q1eo) (No need for decompressing).\n- S2. Decompress the original zip file, you can get a `pytorch_model.bin`.\n- S3. put `pytorch_model.bin` under path `Quick_demo/`.\n- S4. python `test.py` and you can get a conversation as: \n\n > Input: ![](\"https://github.com/chaoyi-wu/RadFM/blob/main/Quick_demo/view1_frontal.jpg\")
Can you identify any visible signs of Cardiomegaly in the image? \n > Output: yes \n\nBy the way, never try to perform this in cpu and gpus are all you need :）.\n\n## Pre-train:\nFor re-training a model on our dataset or large-scale testing our pre-train model, you can check ```src```.\n\nSimply, ```train.py``` for training and ```test.py``` for testing.\n\n* Check the [data_csv](https://huggingface.co/datasets/chaoyi-wu/RadFM_data_csv) to get how different datasets are processed and download them into `src/Dataset/data_csv` \n* Modify the path as you disire, and check ```src/train.py``` to pre-train or ```src/train.py``` to test.\n\n## A Detailed Code Explanation:\nIn this part we will introduce the ```src``` directory in detail and the `Quick_demo` is similar.\n\n### Dataset\nIn the `Dataset` directory, there are two main Python files:\n\n- `multi_dataset.py`\n- `multi_dataset_test.py`\n\nThese files are nearly identical in structure and functionality. The primary difference lies in their usage: `multi_dataset.py` is used for **training**, while `multi_dataset_test.py` is used for **testing**.\n\nBoth files define a key class: `multi_dataset`. This class provides a generative training format that supports multiple datasets. When an instance of this class is called to retrieve a sample, it returns a dictionary with the following structure:\n```\n{\n 'vision_x': vision_x,\n 'lang_x':lang_x, \n 'attention_mask': attention_mask, \n 'labels':labels, \n 'loss_reweight': reweight_tensor, \n 'key_words_query': emphasize_words\n}\n``` \nwhere, each means:\n\n- **`vision_x`**: A tensor representing input images, shaped as **3 × H × W × D**, where `3` is the number of channels (RGB). If only 2D images are provided, they are repeated along the depth dimension (`D = 4` by default).\n\n- **`lang_x`** and **`attention_mask`**: These represent tokenized text inputs and corresponding attention masks. They may include special image placeholders, which are replaced with image embedding tokens during the model's forward pass.\n\n- **`labels`**: Token IDs corresponding to the output text (e.g., answers). As in standard LLM training, a value of `-100` marks tokens that should be ignored in the loss computation. This supports both reconstruction pretraining and instruction tuning (response-only) loss.\n\n- **`loss_reweight`**: An optional tensor used to emphasize specific medical-related terms (e.g., USMLE keywords). It is applied to the per-token autoregressive loss to compute a weighted final loss.\n\n- **`key_words_query`**: Currently unused. It was part of an earlier experiment involving query-based classification loss. You can safely ignore this field by setting it to an empty list (`[]`).\n\nThen in the sub-directory `dataset`, it contains many detailed dataset-wise classes along with our used prompt for organizing them into generative training style, if you want to see how we prompt different dataset you show check the correponding file carefully. For example the chestxray diagnosis dataset listed in our paper are unfiedly preprocess and prompted in `chestxray.py` with the prompt format listed in `yes_no_prompt.json`.\n\n### My_Trainer and datasampler.py\n\n`My_trainer` is a customized version of the `trainer.py` module from `transformers==4.28.1`. The main motivation for creating this separate trainer file is that the original `Trainer` class does not support passing a custom `data_sampler` during `DataLoader` initialization.\n\nOur goal is to **prevent mixing 2D and 3D data within the same training batch**, which can lead to significant overhead when trying to unify the tensor dimensions. By controlling the sampling strategy, we can avoid unnecessary data expansion and improve training efficiency.\n\nThe changes in `My_trainer` are clearly marked with the comment tag `### 吴超逸加 ###`, retained in **Chinese** for easier identification and tracking. These modifications can be integrated into any newer version of the `transformers` library as needed.\nThe `data_sampler.py` python file contains a new distributed sampling function implemented to ensure proper batch organization. It samples either **2D** or **3D** data exclusively within a single batch. This design avoids the computational cost of dynamically expanding 2D data to match 3D inputs when they are mixed in a batch.\n\n### train.py and test.py\n\nThe two python files are easy to understand. `train.py` is used to train the model including pre-training and instruction tuning. `test.py` is used to perform testing on different datset. Please check the [data_csv](https://huggingface.co/datasets/chaoyi-wu/RadFM_data_csv) download the used train/test split csv files into `src/Dataset/data_csv` along with the image sources from different dataset official website and ensure the image path witten in the csv files have been changed to your local path, then you can run the `test.py` successfully. Please ensure you have at least one Nvidia A100 (80GB) to surpport the inference, otherwise it will be quite slow that you can never obtain the results. The output csv file will be like that presented in `src/output_csv_example/caption_example.csv` (an output example for chestxray report generation). You can compare your output format with it to check whether your code is right. Notably, in `test.py`. we adopt inference batch size as one by default to avoid some necessary padding. You can change it to a larger size but please ensure your padding tokens~(shoud be left padding) and the attention mask is set correctly according to the classic LLM batch-wise generation guideline. Otherwise the model cannot output correctly due to take the padding token into foward caculation.\n\n### Model\n\nThe main python files in the Model path are two, i.e., `RadFM//multimodality_model.py` and `RadFM/my_embedding_layer.py`. In the `multimodality_model.py`, it defines a class `MultiLLaMAForCausalLM`, it is similar to classic `CausalLM` classes. The forward function in this class is response for the LLM-based fusion and decoding process. As shown by the code, it will first call\n```\n input_embedding,loss_match= self.embedding_layer(lang_x, vision_x,key_words_query) \n``` \nthat the `self.embedding_layer` is defined by `RadFM/my_embedding_layer.py`. The `input_embedding` is the visual-text mixed token embedding sequancing. `loss_mathc` is related to `key_words_query` input and samely, is aborted now, that always equals zero. Then the forward functin will take the `input_embedding` into any LLMs to obtain the final textual generation and calculate the auto-regressive loss based on the input labels and loss_reweight\n\nIn `my_embedding_layer.py`, the vision input is first processed using a **3D Vision Transformer (ViT)** and a **Perceiver** model. This results in a set of image tokens with shape **S × 32 × d**, where:\n\n- `S` is the number of images (or scans) in a training sample,\n- `32` is a fixed token length per image,\n- `d` is the embedding dimension.\n\nThese generated image tokens are then appended to the language token embedding layer, resulting in an expanded embedding matrix of shape **(Vocab_size + 32 × S) × d**. This design allows each image token to be referenced using special placeholder token IDs that are defined as vocab_size+n in expanded LLM tokenizer.\n\nBy structuring the embedding layer in this way, we canperform the standard token embedding lookup mechanism without requiring explicit loops to insert the token embeddings into textual and reorganize the lenthy embedding output again during the forward pass.\n\nWhen the batch size `B` is greater than 1, each sample in the batch may have different image tokens. Therefore, during the forward pass, each training sample must be **matched with its own corresponding expanded embedding layer**, i.e., the expanded matrixs is of size **B × (Vocab_size + 32 × S) × d**to correctly replace the image placeholders with the appropriate image encoding features.\n\n## Case Study:\n\nSome cases produced by our final model:\n\n![](\"https://github.com/chaoyi-wu/RadFM/blob/main/Images/result_vqa.jpg\"/)
\n![](\"https://github.com/chaoyi-wu/RadFM/blob/main/Images/result_report.jpg\"/)
\n![](\"https://github.com/chaoyi-wu/RadFM/blob/main/Images/result_rationale.jpg\"/)
\n\n## Dataset-Links:\nDatasets downloading URL:\n\n| Dataset Name | Link | Access |\n|--------------|------|--------|\n| Rad3D-series | - | Please mail the Radiopaedia team to obtain access approvement. Then we can share with you. |\n| MPx-series | - | Download from the official websit. |\n| PMC-Figures| https://pan.baidu.com/s/1Src_rhXsaOFp8zJ_3zMFsQ?pwd=p3ne | Open Access |\n| PMC-Inline | https://huggingface.co/datasets/chaoyi-wu/PMC-Inline | Open Access |\n| PMC-CaseReport | [Original version](https://huggingface.co/datasets/chaoyi-wu/PMC-CaseReport_original), [Filtered version](https://huggingface.co/datasets/chaoyi-wu/PMC-CaseReport) | Open Access |\n| VinDr-Mammo | https://www.physionet.org/content/vindr-mammo/1.0.0/ | Credentialed Access |\n| VinDr-SpineXR | https://www.physionet.org/content/vindr-spinexr/1.0.0/ | Credentialed Access |\n| VinDr-PCXR | https://physionet.org/content/vindr-pcxr/1.0.0/ | Credentialed Access |\n| PMC-OA | https://huggingface.co/datasets/axiong/pmc_oa_beta | Open Access |\n| PMC-VQA | https://huggingface.co/datasets/xmcmic/PMC-VQA | Open Access |\n| VQA-RAD | https://osf.io/89kps/| Open Access |\n| SLAKE | https://www.med-vqa.com/slake/ | Open Access |\n| MIMIC-CXR | https://physionet.org/content/mimic-cxr/2.0.0 | Credentialed Access |\n| VinDr-CXR | https://physionet.org/content/vindr-cxr/1.0.0/ | Credentialed Access |\n| NIH ChestXray14 | https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345 | Open Access |\n| CheXpert | https://aimi.stanford.edu/chexpert-chest-x-rays | Open Access |\n| Covid-CXR2 | https://www.kaggle.com/datasets/andyczhao/covidx-cxr2 | Open Access |\n| NLM-TB | [Montgomery](https://openi.nlm.nih.gov/imgs/collections/NLM-MontgomeryCXRSet.zip), [ChinaSet](https://openi.nlm.nih.gov/imgs/collections/ChinaSet_AllFiles.zip) | Open Access |\n| Object-CXR | https://web.archive.org/web/20201127235812/https://jfhealthcare.github.io/object-CXR/ | Open Access |\n| OpenI | https://www.kaggle.com/datasets/raddar/chest-xrays-indiana-university | Open Access |\n| RSNA| https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/rsna-pneumonia-detection-challenge-2018| Open Access |\n| SIIM-ACR | https://www.kaggle.com/datasets/jesperdramsch/siim-acr-pneumothorax-segmentation-data| Open Access |\n\nThe split of each dataset can be found in https://huggingface.co/datasets/chaoyi-wu/RadFM_data_csv you just need to download the image part from each datasets.\n\n## Dataset Codes and Files Linking:\nCheck the following table to see how to process each dataset and how each file in https://huggingface.co/datasets/chaoyi-wu/RadFM_data_csv is linked to each dataset:\n\n| Dataset Name | Process Dataset Code | Related Filename |\n|--------------|------|--------|\n| Rad3D-series | [jpg2nii Process Code](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/jpg2nii_data_convert.py), [nii2npy Process Code](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/nii2npy_for_radiopaedio.py), [Final Datset to Read npy and Related Texts](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/radiopaedia.py) | radiology_article_npy_train/test.json |\n| MPx-series | [MedPix Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/MedPix_dataset.py) | MedPix_muli_train/test.csv, MedPix_single_train/test.csv|\n| PMC-Inline | [Paper-inline Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/paper_inline.py) | paper_train.csv (This dataset is not used for evaluation) |\n| PMC-CaseReport | [Case-report Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/case_report.py) | filtered_case_report_train/test.csv |\n| VinDr-Mammo | [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py) | mammo_balance_train/test.csv |\n| VinDr-SpineXR | [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py) | spinexr_balance_train/test.csv |\n| VinDr-PCXR | [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py) | pcxr_balance_train/test.csv |\n| PMC-OA | [Pmcoa Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/pmcoa.py) | pmcoa_image_caption_train/test.csv |\n| PMC-VQA | [vqa Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/vqa.py) | pmcvaq_train/test.csv|\n| VQA-RAD | [vqa Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/vqa.py)| vqarad_train/test.csv |\n| SLAKE | [vqa Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/vqa.py) | slakevqa_train/test.csv |\n| MIMIC-CXR | [CXR Open Captioning Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py) | mimic_caption_train/test.csv |\n| VinDr-CXR | [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py) | chestxray_balance_train_new.csv, chestxray_balance_test.csv|\n| NIH ChestXray14 | [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py) | chestxray_balance_train_new.csv, chestxray_balance_test.csv |\n| CheXpert | [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py) | chestxray_balance_train_new.csv, chestxray_balance_test.csv |\n| Covid-CXR2 | [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py) | chestxray_balance_train_new.csv, chestxray_balance_test.csv |\n| NLM-TB | [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py) | chestxray_balance_train_new.csv, chestxray_balance_test.csv |\n| Object-CXR | [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py) | chestxray_balance_train_new.csv, chestxray_balance_test.csv |\n| OpenI | [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py) | chestxray_balance_train_new.csv, chestxray_balance_test.csv |\n| RSNA| [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py)| chestxray_balance_train_new.csv, chestxray_balance_test.csv |\n| SIIM-ACR | [Diagnosis Open Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/chestxray.py), [Diagnosis Close (yes/no) Format Dataset](https://github.com/chaoyi-wu/RadFM/blob/main/src/Dataset/dataset/binary.py) | chestxray_balance_train_new.csv, chestxray_balance_test.csv|\n\n\n## Acknowledgment:\nWe sincerely thank all the contributors who uploaded the relevant data in our dataset online. We appreciate their willingness to make these valuable cases publicly available.\n\n## Contact\nIf you have any questions, please feel free to contact wtzxxxwcy02@sjtu.edu.cn.\n"
},
{
"path": "requirements.txt",
"content": "einops==0.6.1\neinops-exts==0.0.4\nhuggingface-hub==0.16.4\nnibabel==5.1.0\nnmslib==2.1.1\nopencv-python==4.8.0.76\npandas==2.0.3\nPillow==9.4.0\npytz==2023.3\nPyYAML==6.0.1\nscikit-learn==1.3.0\nscipy==1.11.2\nscispacy\nsentencepiece==0.1.99\nSimpleITK==2.2.1\nspacy==3.6.1\nspacy-alignments==0.9.0\nspacy-legacy==3.0.12\nspacy-loggers==1.0.4\nspacy-transformers==1.2.5\ntokenizers==0.13.3\ntorch==2.0.1\ntorchaudio==2.0.2\ntorchvision==0.15.2\ntqdm==4.66.1\ntransformers==4.28.1\n"
},
{
"path": "src/Dataset/dataset/MedPix_dataset.py",
"content": "# Import necessary libraries for data processing, image handling, and model integration\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport transformers\nimport pandas as pd\nimport copy \nimport random \nimport os\nimport numpy as np\nimport tqdm\nimport torch\nimport json\nfrom PIL import Image\nimport torchvision\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer\nfrom torchvision import transforms\nfrom ast import literal_eval\nimport re\nimport math\n\nclass MedPix_Single_Dataset(Dataset):\n \"\"\"\n Dataset class for single-image MedPix data.\n \n Processes single medical images with various prompts related to modality,\n plane orientation, and general image captioning.\n \"\"\"\n def __init__(self, csv_path, img_root=\"/gpfs/home/cs/leijiayu/data/MedPix/images/\", down_sample_ratio=5):\n \"\"\"\n Initialize the dataset.\n \n Args:\n csv_path: Path to CSV file containing image metadata\n img_root: Root directory for images\n down_sample_ratio: Factor to reduce dataset size\n \"\"\"\n self.case_list = pd.read_csv(csv_path)\n self.img_root = img_root\n # Image transformation pipeline\n self.transform = transforms.Compose([ \n transforms.RandomResizedCrop([512, 512], scale=(0.8, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n transforms.ToTensor(),\n # normalize, # Commented out normalization\n ]) \n self.down_sample_ratio = down_sample_ratio\n \n # Define template prompts for different tasks\n self.promt = {\n # Image captioning prompts\n \"caption\": [\n \"Describe this input image.\",\n \"Help captioning the image.\",\n \"What can be inflected from the scan?\",\n \"Can you give a caption for this image?\",\n \"Can you provide a brief summary of the radiology image?\",\n \"Please write a report about the image?\",\n \"Can you provide an analysis of this image?\",\n \"Can you explain what is shown in this image?\",\n \"What can be indicated from the radiologic scans?\",\n \"What can you infer from this photograph?\",\n ],\n # Modality identification prompts\n \"modality\": [\n \"What is the modality of the image?\",\n \"What type of imaging technique was utilized?\",\n \"What imaging technology was used?\",\n \"Please tell me the modality of the image.\",\n \"Describe the modality of the image.\",\n \"Tell me the imaging technology used.\",\n \"Can you specify the imaging modality used?\",\n \"What kind of imaging modality was applied?\",\n \"Which imaging technique was used for this image?\",\n \"Could you identify the imaging modality of this picture?\",\n \"What type of image modality was used here?\",\n \"Can you describe the imaging technique used?\"\n ],\n # Plane orientation prompts\n \"plane\": [\n \"Please distinguish the plane of the image\",\n \"Which view does this scan take from?\",\n \"Describe the position.\",\n \"What angle is this image taken from?\",\n \"Can you explain the orientation of this picture?\",\n \"From which direction was this shot taken?\",\n \"Can you specify the plane of this picture?\",\n \"From which standpoint is this image taken?\",\n \"Tell me which plane is the image.\",\n \"From what angle is this picture captured?\",\n \"Can you determine the shot direction of this image?\",\n \"Can you describe the plane of this image?\",\n ],\n # Yes/no prompts for modality\n \"modality_yes_no\": [\n \"Is this image shot in {object}?\",\n \"Is this image in {object}?\",\n \"Is {object} used fro this image?\",\n \"Was this picture taken in {object}?\",\n \"Was this photo captured in {object}?\",\n \"Did they use {object} for this image?\",\n \"Is this picture from {object}?\",\n \"Is this scan shot in {object}?\"\n ],\n # Yes/no prompts for plane orientation\n \"plane_yes_no\": [\n \"Is this image shot from {object} view?\",\n \"Is this image in the view of {object}?\",\n \"Was this scan in {object} view?\",\n \"Is this photo shot in {object} position?\",\n \"Was this picture taken from the perspective of {object}?\",\n \"Is this image captured from {object} viewpoint?\",\n \"Is this photograph from the angle of {object}?\",\n \"Is this snapshot from the view of {object}?\",\n ],\n }\n \n # Lists of possible values for modality and plane categories\n self.sample_list = { \n 'modality': ['HE - High Power (>200X)', 'MR - FLAIR', 'Mammograph', 'SPECT', \n 'MR - FLAIR w/Gd', 'UGI - Upper GI', 'OPHTH - Fundoscopy', 'SBFT - Small Bowel', \n 'Special Stain (specify in caption)', 'EM - Electron Microscopic',\n 'MR T2* gradient GRE', 'CT - Montage', 'ECG EKG', 'MR - T2 FLAIR w/Contrast', \n 'CT - noncontrast', 'MR - ADC Map (App Diff Coeff)', 'Interventional Procedure', \n 'BE - Barium Enema', 'HE - Low Power (<50x)', 'MR - T2 weighted', 'MR - T1W w/Gd (fat suppressed)', \n 'AN - Angiogram', 'OR - Operative photograph', 'Montage of Images', 'XR - Plain Film', \n 'MR - T1W - noncontrast', 'BAS - Barium Swallow', 'US - Ultrasound', 'LOGO', \n 'HE - Med Power (~50-200x)', 'NM - Nuclear Medicine', 'GR - Gross photograph', \n 'MR - Other Pulse Seq.', 'Dermatology', 'IVP/IVU - Intravenous Urogram/Pyelogram', \n 'VCUG - Voiding Cystourethrogram', 'CT - GI Contrast', 'MRS - Spectroscopy', 'MR - Montage', \n 'Photograph', 'MRA - MR Angiography/Venography', 'MR - T1W w/Gadolinium', 'HSG - Hysterosalpingogram', \n 'MR T2* gradient,GRE,MPGR,SWAN,SWI', 'Histology - Special Stain (specify in caption)', 'Venogram', \n 'Arthrogram', 'CT - Myelogram', 'US-D - Doppler Ultrasound', 'CT - GI & IV Contrast', \n 'CP - Clinical photograph', 'Histology (NOS)', 'Not Assigned', 'MR - PDW Proton Density', \n 'CT w/contrast (IV)', 'OPHTH - Slit-Lamp', 'CTA - CT Angiography', 'AN - Angiogram (Catheter)', \n 'MR - T1W SPGR', 'Tomography', 'EP - Endoscopy', 'PET-CT Fusion', 'MR - DWI Diffusion Weighted', \n 'Drawing', 'PET - Positron Emission', 'SPECT - Single Photon', 'RU - Retrograde Urogram', \n 'Myelogram', 'Fundoscopy', 'Virtual Colonoscopy', 'Photographs', \n 'Interventional Procedure (specify in caption)', 'MR - STIR', 'MR - FIESTA'],\n 'plane': ['Other View (see caption)', \n 'Mammo - CC', 'Sagittal', 'Image Plane', 'Mammo - XCC', 'Lateral', 'Longitudinal', \n 'Mammo - Mag CC', 'Frontal', 'Mammo - MLO', 'Transverse', 'Gross Pathology', 'Dermatology', \n '3D Reconstruction', 'Photograph', 'Histology', 'PA', 'Decubitus', 'Multiple or Montage', \n 'Oblique', 'AP', 'Drawing', 'Axial', 'Coronal'],\n }\n \n \n def __len__(self):\n \"\"\"Return effective length of dataset after downsampling\"\"\"\n return math.ceil(len(self.case_list)/self.down_sample_ratio)\n \n def get_image(self, img_path):\n \"\"\"\n Load and preprocess an image\n \n Args:\n img_path: Path to the image file\n \n Returns:\n Processed image tensor with shape [C, H, W, 1]\n \"\"\"\n image = Image.open(img_path).convert('RGB') \n image = self.transform(image)\n image = image.unsqueeze(-1) # Add depth dimension [C, H, W, 1]\n return image\n \n \n def __getitem__(self, idx):\n \"\"\"\n Get a single sample from the dataset\n \n Args:\n idx: Index of the sample to retrieve\n \n Returns:\n Dictionary containing processed sample with image, question, and answer\n \"\"\"\n # Apply downsampling with random offset\n idx = (self.down_sample_ratio*idx + random.randint(0, self.down_sample_ratio-1)) % len(self.case_list)\n sample = self.case_list.iloc[idx]\n answer = sample['context']\n \n # Handle different question types\n if sample['type'] == \"modality\" or sample['type'] == \"plane\":\n pp = random.random()\n if pp > 0.5:\n # Direct question about modality or plane\n question = random.sample(self.promt[sample['type']], 1)[0]\n else:\n # Yes/no question about modality or plane\n question = random.sample(self.promt[sample['type']+'_yes_no'], 1)[0]\n ppp = random.random()\n if ppp > 0.5:\n # True case - format question with correct attribute\n question = question.format(object=answer)\n answer = 'yes'\n else:\n # False case - randomly select a different attribute\n sample_list = self.sample_list[sample['type']]\n try:\n sample_list.remove(answer)\n except:\n pass\n answer = random.sample(sample_list, 1)[0]\n question = question.format(object=answer)\n answer = 'no' \n else:\n # For other types, just select a random prompt\n question = random.sample(self.promt[sample['type']], 1)[0]\n \n # Randomly decide where to position the image - before or after question\n p = random.random()\n images = []\n if p > 0.5:\n try:\n # Place image after question\n images.append(\n {\n \"image\": self.get_image(self.img_root+sample['name']),\n \"position\": {\n \"question\": len(question)\n }\n }\n ) \n except:\n pass\n else:\n try:\n # Place image before question\n images.append(\n {\n \"image\": self.get_image(self.img_root+sample['name']),\n \"position\": {\n \"question\": 0 \n }\n }\n ) \n except:\n pass \n \n # Return formatted sample\n return {\n \"image_dict\": images,\n \"question\": str(question),\n \"answer\": str(answer),\n }\n\nclass MedPix_Multi_Dataset(Dataset):\n \"\"\"\n Dataset class for multi-image MedPix data.\n \n Processes cases with multiple medical images and supports various \n diagnostic and analytical prompts.\n \"\"\"\n def __init__(self, csv_path, img_root=\"/gpfs/home/cs/leijiayu/data/MedPix/images/\"):\n \"\"\"\n Initialize the dataset.\n \n Args:\n csv_path: Path to CSV file containing case metadata\n img_root: Root directory for images\n \"\"\"\n self.case_list = pd.read_csv(csv_path)\n self.img_root = img_root\n # Image transformation pipeline\n self.transform = transforms.Compose([ \n transforms.RandomResizedCrop([512, 512], scale=(0.8, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n transforms.ToTensor(),\n # normalize, # Commented out normalization\n ]) \n \n # Define template prompts for different diagnostic tasks\n self.promt = {\n # Treatment and follow-up prompts\n \"txFollowup\": [\n \"What treatment should the patient take?\",\n \"Please give me some treatment advise.\",\n \"What is the recommended treatment for this condition?\",\n \"What kind of treatment is necessary for this patient?\",\n \"Can you suggest a suitable treatment for this case?\",\n \"What treatment options are available for this patient?\",\n \"What is the best course of treatment for this condition?\",\n \"How to follow up with the patient?\",\n \"What treatment should be administered for this illness?\",\n \"What is the most effective treatment for this disease?\"\n ],\n # Differential diagnosis prompts\n \"ddx\": [\n \"What illness can you diagnose from this images?\",\n \"What disease is shown in the scans?\",\n \"Please make diagnosis with the input images?\",\n \"What health issue can be inferred from these photos?\",\n \"What is the diagnosis based on these medical scans?\",\n \"Based on these scans, what is the patient suffering from?\",\n \"What ailment can be deduced from these medical images?\",\n \"Can you determine the illness from these medical photos?\",\n \"Can you identify the disease from these scans?\",\n \"What is the medical diagnosis based on these images?\",\n ],\n # Diagnostic method prompts\n \"dxHow\": [\n \"What imaging technology is used for diagnosis?\",\n \"What imaging tests are shown in the images?\",\n \"What type of imaging technique is used in medical diagnosis?\",\n \"What kind of imaging technology is used for medical purposes?\",\n \"Which imaging tests are depicted in these pictures?\",\n \"Can you identify the imaging tests in these images?\",\n \"What kind of imaging technology is used in healthcare?\",\n \"What imaging procedures are used for diagnosing diseases?\",\n \"Can you name the imaging tests shown in these photographs?\",\n \"Please distinguish the imaging type in these images\",\n ],\n # General diagnosis prompts\n \"diagnosis\": [\n \"What condition can be diagnosed from these pictures?\",\n \"Can you interpret the disease from these medical scans?\",\n \"What medical condition is depicted in these images?\",\n \"Based on these images, what could be the potential diagnosis?\",\n \"What health condition can be identified from these scans?\",\n \"Can you diagnose the disease from these medical images?\",\n \"What is the patient's condition according to these scans?\",\n \"What medical issue can be determined from these photos?\",\n \"Can you infer the illness from these medical scans?\",\n \"What is the probable diagnosis based on these medical images?\",\n \"What illness can you diagnose from this images?\",\n \"What disease is shown in the scans?\",\n \"Please make diagnosis with the input images?\",\n \"What health issue can be inferred from these photos?\",\n \"What is the diagnosis based on these medical scans?\",\n \"Based on these scans, what is the patient suffering from?\",\n \"What ailment can be deduced from these medical images?\",\n ], \n # Findings description prompts\n \"findings\": [\n \"Caption the case.\",\n \"Describe your findings for this patient.\",\n \"What is shown in the case?\",\n \"Please help me write a report about the patient.\",\n \"Can you provide a summary of the case?\",\n \"What are the key points in this case?\",\n \"Could you explain the details of the case?\",\n \"What are your observations about the case?\",\n \"Can you give an overview of the case?\",\n \"How would you interpret this case?\",\n \"What is your analysis of the patient?\",\n \"Can you provide a brief on the patient?\"\n ],\n # Exam result prompts\n \"exam\": [\n \"Make a conclusion for this patient.\",\n \"What are the exam results for this patient?\",\n \"What is the diagnosis for this patient?\",\n \"What are the symptoms presented by this patient?\",\n \"Please make diagnosis with the input case.\",\n \"Is there any abnormality with the presented case?\",\n \"What can be reflected from the input images?\",\n \"Please provide me with some diagnosis advise.\",\n \"Can you provide a summary of the patient's condition?\",\n \"Can you provide a detailed analysis of the patient's condition?\"\n ],\n # Case discussion prompts\n \"discussion\": [\n \"Discuss about the case more.\",\n \"Tell more about the patient's illness.\",\n \"What image patterns or knowledge can help you make diagnosis?\",\n \"Could you provide more details about the situation?\",\n \"What additional information can you provide about the issue?\",\n \"Can you explain more about the subject matter?\",\n \"What other factors should be considered in this scenario?\",\n \"Can you provide more context or background information?\",\n \"What other relevant details can you share about this case?\", \n \"Can you expand on your initial explanation?\" ,\n \"What other insights can you provide on this matter?\" ,\n \"Can you delve deeper into the specifics of the situation?\",\n ],\n } \n \n def __len__(self):\n \"\"\"Return the total number of cases in the dataset\"\"\"\n return len(self.case_list)\n \n def get_image(self, img_path):\n \"\"\"\n Load and preprocess an image\n \n Args:\n img_path: Path to the image file\n \n Returns:\n Processed image tensor with shape [C, H, W, 1]\n \"\"\"\n image = Image.open(img_path).convert('RGB') \n image = self.transform(image)\n image = image.unsqueeze(-1) # Add depth dimension [C, H, W, 1]\n return image\n \n \n def __getitem__(self, idx):\n \"\"\"\n Get a single case from the dataset\n \n Args:\n idx: Index of the case to retrieve\n \n Returns:\n Dictionary containing processed case with images, question, and answer\n \"\"\"\n sample = self.case_list.iloc[idx]\n \n # Clean up answer text by removing bullet points\n answer = str(sample['context']).replace('• ', '')\n \n # Select random prompt for the specific task type\n question = random.sample(self.promt[sample['type']], 1)[0]\n \n # Optionally prepend patient history to the question\n history = sample['history']\n if history is not None:\n p = random.random()\n if p > 0.5:\n try:\n question = history + ' ' + question\n except:\n pass\n \n # Process all images associated with this case\n image_names = sample['name'].split(',')\n p = random.random()\n images = []\n \n # Randomly decide whether to put images after or before question\n if p > 0.5:\n # Place images after question\n for pp in image_names:\n try:\n images.append(\n {\n \"image\": self.get_image(self.img_root+pp),\n \"position\": {\n \"question\": len(question)\n }\n }\n ) \n except:\n pass\n else:\n # Place images before question\n for pp in image_names:\n try:\n images.append(\n {\n \"image\": self.get_image(self.img_root+pp),\n \"position\": {\n \"question\": 0\n }\n }\n ) \n except:\n pass\n \n # For findings, remove measurements which might be distracting\n if sample['type'] == \"findings\":\n pattern = r\"\\d+(\\.\\d+)?\\s*(mm|cm|x\\d+\\s*cm)\"\n answer = re.sub(pattern, \"\", answer)\n \n # Limit number of images to prevent memory issues\n if len(images) > 10:\n images = random.sample(images, 10)\n \n # Return formatted case\n return {\n \"image_dict\": images,\n \"question\": str(question),\n \"answer\": str(answer),\n }\n\nclass MedPix_QA_Dataset(Dataset):\n \"\"\"\n Dataset class for MedPix question-answer pairs.\n \n Processes medical QA pairs with associated images.\n \"\"\"\n def __init__(self, csv_path, img_root=\"/gpfs/home/cs/leijiayu/data/MedPix/images/\"):\n \"\"\"\n Initialize the dataset.\n \n Args:\n csv_path: Path to CSV file containing QA pairs\n img_root: Root directory for images\n \"\"\"\n self.case_list = pd.read_csv(csv_path)\n self.img_root = img_root\n # Image transformation pipeline\n self.transform = transforms.Compose([ \n transforms.RandomResizedCrop([512, 512], scale=(0.8, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n transforms.ToTensor(),\n # normalize, # Commented out normalization\n ]) \n \n def __len__(self):\n \"\"\"Return the total number of QA pairs in the dataset\"\"\"\n return len(self.case_list)\n \n def get_image(self, img_path):\n \"\"\"\n Load and preprocess an image\n \n Args:\n img_path: Path to the image file\n \n Returns:\n Processed image tensor with shape [C, H, W, 1]\n \"\"\"\n image = Image.open(img_path).convert('RGB') \n image = self.transform(image)\n image = image.unsqueeze(-1) # Add depth dimension [C, H, W, 1]\n return image\n \n \n def __getitem__(self, idx):\n \"\"\"\n Get a single QA pair from the dataset\n \n Args:\n idx: Index of the QA pair to retrieve\n \n Returns:\n Dictionary containing processed QA pair with image, question, and answer\n \"\"\"\n sample = self.case_list.iloc[idx]\n \n # Extract question, answer and explanation\n answer = sample['answer']\n question = sample['question']\n explanation = sample['explanation']\n \n # Combine answer with explanation when available\n try:\n answer = answer + '. ' + explanation\n except:\n pass\n \n # Randomly decide whether to place image before or after question\n p = random.random()\n images = []\n if p > 0.5:\n # Place image after question\n try:\n images.append(\n {\n \"image\": self.get_image(self.img_root+sample['name']),\n \"position\": {\n \"question\": len(question)\n }\n }\n ) \n except:\n pass\n else:\n # Place image before question\n try:\n images.append(\n {\n \"image\": self.get_image(self.img_root+sample['name']),\n \"position\": {\n \"question\": 0 \n }\n }\n ) \n except:\n pass \n \n # Limit number of images to prevent memory issues\n if len(images) > 10:\n images = random.sample(images, 10) \n \n # Return formatted QA pair\n return {\n \"image_dict\": images,\n \"question\": str(question),\n \"answer\": str(answer),\n }\n \n# Example usage (commented out)\n# dataset = MedPix_Single_Dataset(csv_path = '/gpfs/home/cs/leijiayu/data/MedPix/Preprocessor/MedPix_single_train.csv')\n# for i in tqdm.tqdm(range(len(dataset))):\n# sample = dataset[i]\n# print(len(sample['image_dict']), sample['image_dict'][0][\"image\"].shape, sample['question'], sample['answer'])\n# input()\n\n# dataset = MedPix_Multi_Dataset(csv_path = '/gpfs/home/cs/leijiayu/data/MedPix/Preprocessor/MedPix_multi_train.csv')\n# for i in tqdm.tqdm(range(len(dataset))):\n# sample = dataset[i]\n# print(len(sample['image_dict']), sample['image_dict'][0][\"image\"].shape, sample['question'], sample['answer'])\n# input()\n \n# dataset = MedPix_QA_Dataset(csv_path = '/gpfs/home/cs/leijiayu/data/MedPix/Preprocessor/MedPix_questions_train.csv')\n# for i in tqdm.tqdm(range(len(dataset))):\n# sample = dataset[i]\n# print(len(sample['image_dict']), sample['image_dict'][0][\"image\"].shape, sample['question'], sample['answer'])\n# input()"
},
{
"path": "src/Dataset/dataset/__init__.py",
"content": "from .radiopaedia import RadioVQA_Dataset,Radio_Modality_Dataset,Radiofeatures_Dataset,RadioCaption_Dataset\nfrom .binary import Binary_Dataset\nfrom .chestxray import ChestXray_Dataset\nfrom .vqa import VQA_Dataset\nfrom .pmcoa import PMCOA_Dataset\nfrom .paper_inline import Paper_Inline_dataset\nfrom .case_report import CaseReport_dataset\nfrom .MedPix_dataset import MedPix_Multi_Dataset,MedPix_Single_Dataset,MedPix_QA_Dataset\n"
},
{
"path": "src/Dataset/dataset/binary.py",
"content": "import csv\nimport json\nimport logging\nimport os\nimport re\nimport difflib\nimport sys\nimport torch\nimport random\nfrom abc import abstractmethod\nfrom itertools import islice\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union\nfrom collections.abc import Mapping\nfrom torch.utils.data import DataLoader\nimport PIL\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom torchvision import transforms\nfrom collections import defaultdict\nfrom PIL import Image\n\nclass Binary_Dataset(Dataset):\n \"\"\"_summary_\n Args:\n Dataset (_type_): caption task formulated as vqa task for Chestxray classification dataset\n csv_path (_type_): path to csv file\n prompt_json_file (_type_): path to json file containing binary cls prompts, the answer is yes/no\n Output:\n Dict: {\n \"image_dict\": {\"image\": image, \"position\": {\"question\": 0}}, # image is a tensor of shape [c,w,h,d] [3,512,512,1], position is a dict, random choice of 0 or len(question)\n \"question\": question, # random choice of caption prompts\n \"answer\":answer, # caption\n }\n \"\"\"\n def __init__(self,csv_path,prompt_json_file):\n data_info = pd.read_csv(csv_path)\n self.img_path_list = np.asarray(data_info['image_path'])\n self.disease_list = np.asarray(data_info['disease'])\n self.answer_list = np.asarray(data_info['label'])\n self.transform = transforms.Compose([ \n transforms.RandomResizedCrop([512,512],scale=(0.8, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n transforms.ToTensor(),\n ]) \n with open(prompt_json_file, 'r') as f:\n self.caption_prompts = json.load(f)['caption_prompt']\n self.map_answer = {0:'no',1:'yes'}\n \n def __len__(self):\n return len(self.img_path_list)\n\n def __getitem__(self, index):\n img_path = self.img_path_list[index]\n image = Image.open(img_path).convert('RGB') \n image = self.transform(image)\n image = image.unsqueeze(-1) # c,w,h,d\n answer = self.map_answer[self.answer_list[index]]\n question = random.choice(self.caption_prompts).replace('disease',self.disease_list[index])\n image_dict = [{\n \"image\": image,\n \"position\": {\n \"question\": len(question)\n }\n }]\n return {\n \"image_dict\": image_dict,\n \"question\": question,\n \"answer\":answer,\n }\n "
},
{
"path": "src/Dataset/dataset/caption_prompt.json",
"content": "{\n \"caption_prompt\": [\n \"Can you provide a caption consists of finding and impression for this medical image?\",\n \"Describe the finding and impression of the medical image you see.\",\n \"Please caption this medical scan with finding and impression.\",\n \"What is the finding and impression of this image?\",\n \"Describe this medical scan with finding and impression.\",\n \"Please write a caption consists of finding and impression for this image.\",\n \"Can you summarize with finding and impression the images presented?\",\n \"Please caption this scan with finding and impression.\",\n \"Please provide a caption consists of finding and impression for this medical image.\",\n \"Can you provide a summary consists of finding and impression of this radiograph?\",\n \"What are the findings and impression presented in this medical scan?\",\n \"Please write a caption consists of finding and impression for this scan.\",\n \"Can you provide a description consists of finding and impression of this medical scan?\",\n \"Please caption this medical scan with finding and impression.\",\n \"Can you provide a caption consists of finding and impression for this medical scan?\"\n ]\n}"
},
{
"path": "src/Dataset/dataset/case_report.py",
"content": "# Import necessary libraries for data processing, image handling, and model integration\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport transformers\nimport pandas as pd\nimport copy \nimport random \nimport os\nimport numpy as np\nimport tqdm\nimport torch\nimport json\nfrom PIL import Image\nimport torchvision\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer\nfrom torchvision import transforms\nfrom ast import literal_eval\n\nclass CaseReport_dataset(Dataset):\n \"\"\"\n Dataset class for medical case reports with associated images.\n \n This dataset processes medical case reports containing text and referenced images,\n formatting them for multimodal medical AI training or inference.\n \"\"\"\n def __init__(self, csv_path, img_path):\n \"\"\"\n Initialize the dataset.\n \n Args:\n csv_path: Path to CSV file containing case reports data\n img_path: Base path to the directory containing images\n \"\"\"\n self.img_path = img_path # Root directory for images\n self.question_list = pd.read_csv(csv_path) # Load dataset from CSV\n \n # Define image transformation pipeline\n # normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n self.transform = transforms.Compose([ \n # Crop and resize images to 512x512, maintaining 80-100% of original content\n transforms.RandomResizedCrop([512, 512], scale=(0.8, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n # Convert to tensor with values in [0, 1]\n transforms.ToTensor(),\n # normalize, # Commented out normalization\n ]) \n \n \n def __len__(self):\n \"\"\"Return the total number of samples in the dataset\"\"\"\n return len(self.question_list)\n \n def __getitem__(self, idx):\n \"\"\"\n Get a single sample from the dataset\n \n Args:\n idx: Index of the sample to retrieve\n \n Returns:\n Dictionary containing the processed sample with image, question, and answer\n \"\"\"\n # Get the row from dataframe\n sample = self.question_list.iloc[idx]\n \n # Extract metadata and content\n PMC_id = sample['PMC_id'] # PubMed Central ID\n img_ref = literal_eval(sample['img_ref']) # List of image references\n context = str(sample['context']) # Case context\n \n # Truncate long contexts to focus on beginning and end\n sentences = context.split('.')\n if len(sentences) > 5:\n first_sentence = sentences[0] # Keep the first sentence\n last_sentences = \". \".join(context.split('.')[-4:]) # Keep the last 4 sentences\n context = first_sentence + '. ' + last_sentences\n \n # Format question by combining context and actual question\n question = str(context) + '\\n' + str(sample['question']).replace('Q:', '') \n \n # Clean up answer formatting\n answer = str(sample['answer']).replace('A:', '')\n \n # Process each referenced image\n images = []\n for img_id in img_ref:\n # Construct the full image path\n img_path = self.img_path + '/' + PMC_id + '_' + img_id + '.jpg'\n \n try:\n # Load and transform the image\n image = Image.open(img_path).convert('RGB') \n image = self.transform(image)\n \n # Randomly decide where to place the image in the text\n # Either at the end of question or at the end of context\n if random.random() > 0.5:\n images.append({'image': image, \"position\": {\"question\": len(question)}}) \n else:\n images.append({'image': image, \"position\": {\"question\": len(context)}}) \n except:\n # Skip images that can't be loaded\n continue \n \n # Return formatted sample\n return {\n \"image_dict\": images, # List of images with position information\n \"question\": question, # Formatted question text\n \"answer\": answer, # Answer text\n }\n\n# Example usage (commented out)\n# csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/multi_modal/Data/GPT_realdata/casa_report_train.csv' \n# img_path = '/home/cs/leijiayu/data/all_images/figures/'\n# dataset = CaseReport_dataset(csv_path, img_path)\n# print(dataset[0])"
},
{
"path": "src/Dataset/dataset/chestxray.py",
"content": "import csv\nimport json\nimport logging\nimport os\nimport re\nimport difflib\nimport sys\nimport torch\nimport random\nfrom abc import abstractmethod\nfrom itertools import islice\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union\nfrom collections.abc import Mapping\nfrom torch.utils.data import DataLoader\nimport PIL\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom torchvision import transforms\nfrom collections import defaultdict\nfrom PIL import Image\n\nclass ChestXray_Dataset(Dataset):\n \"\"\"_summary_\n Args:\n Dataset (_type_): caption task formulated as vqa task for Chestxray classification dataset\n csv_path (_type_): path to csv file\n img_root_dir (_type_): path to image root directory \n prompt_json_file (_type_): path to json file containing caption prompts\n Output:\n Dict: {\n \"image_dict\": {\"image\": image, \"position\": {\"question\": 0}}, # image is a tensor of shape [c,w,h,d] [3,512,512,1], position is a dict, random choice of 0 or len(question)\n \"question\": question, # random choice of caption prompts\n \"answer\":answer, # caption\n }\n \"\"\"\n def __init__(self,csv_path,prompt_json_file):\n data_info = pd.read_csv(csv_path)\n self.img_path_list = np.asarray(data_info['image_path'])\n self.answer_list = np.asarray(data_info['label'])\n self.transform = transforms.Compose([ \n transforms.RandomResizedCrop([512,512],scale=(0.8, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n transforms.ToTensor(),\n ]) \n with open(prompt_json_file, 'r') as f:\n self.caption_prompts = json.load(f)['caption_prompt']\n\n def __len__(self):\n return len(self.img_path_list)\n\n def __getitem__(self, index):\n img_path = self.img_path_list[index]\n try:\n image = Image.open(img_path).convert('RGB') \n image = self.transform(image)\n image = image.unsqueeze(-1) # c,w,h,d\n except:\n image = np.random.randn(3,512,512,4)\n \n answer = self.answer_list[index]\n question = random.choice(self.caption_prompts)\n image_dict = [{\n \"image\": image,\n \"position\": {\n \"question\": len(question)\n }\n }]\n return {\n \"image_dict\": image_dict,\n \"question\": question,\n \"answer\":answer,\n }\n \n \nif __name__ == \"__main__\":\n test_dataset = ChestXray_Dataset(csv_path = '../data_csv/chestxray.csv', \n prompt_json_file = './cls_prompt.json')\n for i in range(10):\n test_data = test_dataset[i]\n print(test_data['image_dict'][0]['image'].shape) # [3,512,512,1]\n #需要确保所有的chestxray img_path都有图像\n \n\n\n\n"
},
{
"path": "src/Dataset/dataset/cls_prompt.json",
"content": "{\n \"caption_prompt\": [\n \"What is the diagnosis for this chest X-ray?\",\n \"Based on this X-ray, what type of lung disease is suspected?\",\n \"Can you identify any abnormality in this chest X-ray?\",\n \"What are the findings in this chest X-ray?\",\n \"What pathology is indicated by this chest X-ray?\",\n \"What lung disease is likely present in this chest X-ray?\",\n \"What are the potential causes of the findings in this chest X-ray?\",\n \"What are your conclusions from this chest X-ray?\",\n \"What is your interpretation of this chest X-ray?\",\n \"What abnormalities are present in this chest X-ray?\",\n \"What is the differential diagnosis for the findings in this chest X-ray?\"\n ]\n}"
},
{
"path": "src/Dataset/dataset/data_csv/README.md",
"content": "Please check the [data_csv](https://huggingface.co/datasets/chaoyi-wu/RadFM_data_csv) to download the used train/test split csv files and ensure the image path are related to your local path. "
},
{
"path": "src/Dataset/dataset/dicom_to_png_for_VinDR_sampled_using_mammo.py",
"content": "import os\nimport cv2\nimport csv\nimport json\nimport imageio\n\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\n\nimport matplotlib.pyplot as plt\nfrom pydicom import dcmread\n\ndef dcm_to_png(dcm_path,save_png_path):\n ds = dcmread(dcm_path)\n arr = ds.pixel_array\n img_array = arr.copy()\n cv2.normalize(arr, img_array, 0, 255, cv2.NORM_MINMAX)\n img_array = np.array(img_array,dtype='uint8')\n # img_array = cv2.resize(img_array, (512,512), interpolation = cv2.INTER_LINEAR)\n imageio.imwrite(save_png_path,img_array) \n\ndef preprocess_csv(csv_path,data_dir,save_data_dir):\n data_info = pd.read_csv(csv_path)\n patient_file_list = data_info.iloc[:,0]\n img_file_list = data_info.iloc[:,2]\n for idx in tqdm(range(len(img_file_list))):\n patient_file = patient_file_list[idx]\n img_file = img_file_list[idx]\n img_path = os.path.join(data_dir,str(patient_file),str(img_file)+'.dicom')\n os.makedirs(os.path.join(save_data_dir,str(patient_file)), exist_ok=True)\n save_img_path = os.path.join(save_data_dir,str(patient_file),str(img_file)+'.png')\n dcm_to_png(img_path,save_img_path)\n\n\ncsv_path = './DATA/VinDr/VinDr-Mammo/1.0.0/breast-level_annotations.csv'\ndata_dir = './DATA/VinDr/VinDr-Mammo/1.0.0/images'\nsave_data_dir = './DATA/VinDr/VinDr-Mammo/process/images'\nos.makedirs(save_data_dir, exist_ok=True)\npreprocess_csv(csv_path,data_dir,save_data_dir)\n"
},
{
"path": "src/Dataset/dataset/jpg2nii_data_convert.py",
"content": "#processed cases accoring to case_id_list, and save a csv file, with image path and image caption\nimport os\nimport cv2\nimport csv\nimport json\nimport subprocess\nimport pandas as pd\nimport numpy as np\nimport SimpleITK as sitk\nfrom tqdm import tqdm\nfrom collections import defaultdict\n\ndef get_image(single_image_dir,single_image_filenames):\n # single_image_filenames\n single_image_filenames.sort(key=lambda x: int(x.split('.')[0]))\n image_list = []\n for image_filename in single_image_filenames:\n image_file = os.path.join(single_image_dir, image_filename)\n #read jpeg to 2D array\n image_array = cv2.imread(image_file,0)\n if image_array is not None:\n image_size = image_array.shape\n image_array = cv2.resize(image_array,(512,512),interpolation = cv2.INTER_LINEAR)\n image_list.append(image_array)\n else:\n pass\n image_array = np.array(image_list) #c,w,h\n if len(image_array.shape) == 3:\n if image_array.shape[0] < image_array.shape[1]:\n image_array = image_array.transpose((1, 2, 0))\n # image_array = np.transpose(image_array, (2,0,1)) # w,h,c\n return image_array\n\ngray_list = ['CT','MRI','X-ray','Ultrasound','Mammography']\n\ndef convert_case(case_id,image_root_dir,json_root_dir,save_case_dict,save_root_dir=None):\n # save_image_dir \n case_images_dir = os.path.join(image_root_dir, case_id)\n case_json_path = os.path.join(json_root_dir, case_id+'.json')\n with open(case_json_path, 'r') as f:\n data = json.load(f)\n image_nums = (len(data.keys())-1)//2\n for image_num in range(1,image_nums+1):\n case_dict = defaultdict(list)\n image_dir = os.path.join(case_images_dir, str(image_num)) #./images/1/1\n image_caption = data[str(image_num) + '详情']\n image_modality = data[str(image_num)][0]['modality']\n \n single_image_names = os.listdir(image_dir)\n single_image_names.sort(key=lambda x: int(x.split('_')[1]))\n save_image_series = []\n \n for single_image_name in single_image_names:\n single_image_dir = os.path.join(image_dir, single_image_name)\n \n save_npy_dir = os.path.join(save_root_dir,str(case_id),str(image_num))\n \n \n single_image_filenames = os.listdir(single_image_dir)\n if len(os.listdir(single_image_dir)) == 1:\n # 2D image\n image_file = os.path.join(single_image_dir, single_image_filenames[0])\n save_image_array = cv2.imread(image_file) # w,h,c\n else:\n save_image_array = get_image(single_image_dir,single_image_filenames)\n if not os.path.exists(save_npy_dir):\n os.makedirs(save_npy_dir)\n # print(save_image_array.shape)\n if save_image_array is not None:\n if len(save_image_array.shape) <= 5 and len(save_image_array.shape) >=2:\n save_nii_path = os.path.join(save_npy_dir,single_image_name+'.nii.gz')\n out = sitk.GetImageFromArray(save_image_array)\n sitk.WriteImage(out, save_nii_path)\n save_image_series.append(save_nii_path)\n else:\n save_npy_path = os.path.join(save_npy_dir,single_image_name+'.npy')\n np.save(save_npy_path,save_image_array)\n save_image_series.append(save_npy_path)\n case_dict['image'] = save_image_series\n case_dict['image_caption'] = image_caption\n case_dict['image_modality'] = image_modality\n save_case_dict.append(case_dict)\n \nif __name__ == \"__main__\":\n # case_id,image_root_dir,json_root_dir\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('--index', default=0, type=int)\n parser.add_argument('--add_index', default=0, type=int)\n parser.add_argument('--start_index', default=1, type=int)\n parser.add_argument('--end_index', default=1000, type=int)\n args = parser.parse_args()\n \n image_root_dir = '/mnt/petrelfs/share_data/zhangxiaoman/DATA/Radio_VQA/processed_file/images'\n json_root_dir = '/mnt/petrelfs/share_data/zhangxiaoman/DATA/Radio_VQA/processed_file/jsons'\n save_root_dir = '/mnt/petrelfs/share_data/zhangxiaoman/DATA/Radio_VQA/processed_file/npys'\n save_case_dict = []\n \n args.start_index = args.index*1000+1 + args.add_index\n args.end_index = (args.index+1)*1000+1\n \n for case_id in tqdm(range(args.start_index,args.end_index)):\n case_id = str(case_id)\n convert_case(case_id,image_root_dir,json_root_dir,save_case_dict,save_root_dir)\n # CT_0 (200, 630, 630, 3)\n\n # save to csv\n save_json_file = '/mnt/petrelfs/share_data/zhangxiaoman/DATA/Radio_VQA/processed_file/processed_jsons/processed_json_'+str(args.index)+'.json'\n with open(save_json_file, 'w', encoding='utf-8') as f:\n json.dump(save_case_dict, f, ensure_ascii=False,indent=4)\n # B, S, T, W, H, Z\n # srun --partition=medai --mpi=pmi2 --quotatype=auto --gres=gpu:0 -n1 --ntasks-per-node=1 python data_convert.py --index 2 --add_index 24\n # cd /mnt/petrelfs/share_data/zhangxiaoman/DATA/Radio_VQA/jpeg2npy"
},
{
"path": "src/Dataset/dataset/mammo_prompt.json",
"content": "{\n \"caption_prompt\": [\n \"What is the diagnosis for this mammogram?\",\n \"Based on this X-ray, what type of breast disease is suspected?\",\n \"Can you identify any abnormality in this mammogram?\",\n \"What are the findings in this mammogram?\",\n \"What pathology is indicated by this mammogram?\",\n \"What lung disease is likely present in this mammogram?\",\n \"What are the potential causes of the findings in this mammogram?\",\n \"What are your conclusions from this mammogram?\",\n \"What is your interpretation of this mammogram?\",\n \"What abnormalities are present in this mammogram?\",\n \"What is the differential diagnosis for the findings in this mammogram?\",\n \"What is the diagnosis for this breast X-ray?\",\n \"Can you identify any abnormality in this breast X-ray?\",\n \"What are the findings in this breast X-ray?\",\n \"What pathology is indicated by this breast X-ray?\",\n \"What lung disease is likely present in this breast X-ray?\",\n \"What are the potential causes of the findings in this breast X-ray?\",\n \"What are your conclusions from this breast X-ray?\",\n \"What is your interpretation of this breast X-ray?\",\n \"What abnormalities are present in this breast X-ray?\",\n \"What is the differential diagnosis for the findings in this breast X-ray?\"\n ]\n}"
},
{
"path": "src/Dataset/dataset/modality_prompt.json",
"content": "{\n \"caption_prompt\": [\n \"What modality is used to take this image?\",\n \"What type of imaging modality is used to acquire the above image?\",\n \"What imaging modality is used?\",\n \"What imaging modality was used to take this image?\",\n \"What is the imaging modality?\"\n ],\n \"modality_prompt\": [\n \"Is this image a modality scan?\",\n \"Is the given image a modality scan?\",\n \"Is the given image a modality?\"\n ]\n}\n"
},
{
"path": "src/Dataset/dataset/nii2npy_for_radiopaedio.py",
"content": "import os\nimport cv2\nimport csv\nimport json\nimport subprocess\nimport pandas as pd\nimport numpy as np\nimport SimpleITK as sitk\nfrom tqdm import tqdm\nfrom scipy import ndimage\nfrom collections import defaultdict\n\ndef resize_array(array_list, shape_list):\n if len(array_list) == 0:\n return None\n # Get the median value of the c dimension\n c_values = [shape[3] for shape in shape_list]\n z = np.median(c_values)\n \n # Resize each array to the same size\n resized_arrays = []\n for array in array_list:\n resized_array = ndimage.zoom(array, (3/array.shape[0],512/array.shape[1], 512/array.shape[2], z/array.shape[3]), order=0)\n # print(resized_array.shape)\n if resized_array.shape[3] == z:\n resized_arrays.append(resized_array)\n else:\n if resized_array.shape[3] > z:\n resized_arrays.append(resized_array[:,:,:,:int(z)])\n else:\n resized_arrays.append(np.pad(resized_array, ((0,0),(0,0),(0,0),(0,int(z-resized_array.shape[3]))), 'constant', constant_values=0))\n # Convert the list of arrays to a numpy array\n resized_array = np.array(resized_arrays)\n \n return resized_array\n\ndef process_image_list(image_path_list):\n image_shape_list = []\n image_array_list = []\n for image_path in image_path_list:\n if os.path.exists(image_path) == False:\n continue\n elif image_path.split('.')[-1] == 'npy':\n image_array = np.load(image_path) #c,w,h,d\n try:\n image_array = cv2.resize(image_array,(512,512))\n if len(image_array.shape) == 2:\n image_array = image_array[np.newaxis,:,:,np.newaxis]\n # 1wh1 to 3wh1\n image_array = np.concatenate([image_array,image_array,image_array],axis=0)\n elif len(image_array.shape) == 3:\n #whc to cwh\n image_array = image_array.transpose(2,0,1)[:,:,:,np.newaxis]\n \n image_shape_list.append(image_array.shape)\n image_array_list.append(image_array)\n except:\n pass\n else:\n itk_image = sitk.ReadImage(image_path)\n image_array = sitk.GetArrayFromImage(itk_image) #c,w,h,d\n if image_array.shape[0] != 512:\n image_array = cv2.resize(image_array,(512,512))\n if len(image_array.shape) == 2:\n image_array = image_array[np.newaxis,:,:,np.newaxis]\n image_array = np.concatenate([image_array,image_array,image_array],axis=0)\n elif len(image_array.shape) == 3:\n image_array = image_array[np.newaxis,:,:,:]\n image_array = np.concatenate([image_array,image_array,image_array],axis=0)\n image_shape_list.append(image_array.shape)\n image_array_list.append(image_array)\n save_image_array = resize_array(image_array_list, image_shape_list)\n return save_image_array\n \n \ndef process_json_file(json_file,save_json_file,save_root_dir):\n if not os.path.exists(save_root_dir):\n os.makedirs(save_root_dir)\n with open(json_file, 'r') as f:\n data = json.load(f)\n data_len = len(data)\n for i in tqdm(range(data_len)):\n samples = data[i]['samples']\n for sample_i in tqdm(range(len(samples))):\n if samples[sample_i]['image'] == []:\n samples.pop(sample_i)\n else:\n image_path_list = samples[sample_i]['image']\n case_id = image_path_list[0].split('/')[-3]\n save_image_array = process_image_list(image_path_list)\n if save_image_array is not None:\n save_image_path = os.path.join(save_root_dir, str(case_id)+'_'+str(sample_i)+'.npy')\n np.save(save_image_path,save_image_array)\n # 如果边处理边传到aws的话可以参考这一段\n # save_aws_image_path = save_image_path.replace('/mnt/petrelfs/share_data/zhangxiaoman/DATA/','s3://zhangxiaoman_hdd_new_share/')\n # os.system(f'aws s3 cp {save_image_path} {save_aws_image_path} --endpoint-url=http://10.140.27.254')\n # os.remove(save_image_path)\n # data[i]['npy_path'] = save_aws_image_path\n data[i]['samples']['npy_path'] = save_image_path\n data[i]['samples']['image_size'] = save_image_array.shape\n else:\n print(i,image_path_list)\n if len(samples) == 0:\n data.pop(i)\n \n with open(save_json_file, 'w') as f:\n json.dump(data, f,ensure_ascii=False,indent=4)\n \n\nif __name__ == \"__main__\":\n json_file = '../processed_file/processed_jsons/processed_json_2023-11-18.json'\n save_json_file = '../processed_file/processed_jsons/processed_json_2023-11-18-npy.json'\n save_root_dir = '../processed_file/processed_images'\n\n process_json_file(json_file,save_json_file,save_root_dir)\n "
},
{
"path": "src/Dataset/dataset/paper_inline.py",
"content": "# Import necessary libraries for data processing, image handling, and model integration\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport transformers\nimport pandas as pd\nimport copy \nimport random \nimport os\nimport numpy as np\nimport tqdm\nimport torch\nimport json\nfrom PIL import Image\nimport torchvision\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer\nfrom torchvision import transforms\n\nclass Paper_Inline_dataset(Dataset):\n \"\"\"\n Dataset class for processing scientific papers with inline images.\n \n This dataset extracts text and associated images from scientific papers,\n preparing them for multimodal model training.\n \"\"\"\n def __init__(self, csv_path, img_path, sample_sentence_length=50, max_img_size=3):\n \"\"\"\n Initialize the dataset.\n \n Args:\n csv_path: Path to CSV file containing paper metadata\n img_path: Root directory for paper figures\n sample_sentence_length: Maximum number of sentences to include in a sample\n max_img_size: Maximum number of images to include in a sample\n \"\"\"\n self.max_img_size = max_img_size\n self.sample_sentence_length = sample_sentence_length\n self.img_path = img_path\n # Load paper paths from CSV\n self.paper_path = np.array(pd.read_csv(csv_path)['PMC_path'])\n \n # Define image transformation pipeline\n # normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n self.transform = transforms.Compose([ \n # Crop and resize images to 512x512, maintaining 80-100% of original content\n transforms.RandomResizedCrop([512, 512], scale=(0.8, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n # Convert to tensor with values in [0, 1]\n transforms.ToTensor(),\n # normalize, # Commented out normalization\n ]) \n \n \n def __len__(self):\n \"\"\"Return the total number of papers in the dataset\"\"\"\n return self.paper_path.shape[0]\n \n def __getitem__(self, idx):\n \"\"\"\n Get a single sample from the dataset\n \n Args:\n idx: Index of the paper to retrieve\n \n Returns:\n Dictionary containing the processed sample with images, question, and answer\n \"\"\"\n # Load the paper JSON file\n paper_json = self.paper_path[idx]\n # Extract PMC ID from the file path\n PMC_name = paper_json.rsplit('/', 2)[-1].split('.')[0]\n # Load the list of sentences with image references\n sentences_list = json.load(open(paper_json, 'r'))\n # Process the paper to extract text and images\n image_dict, question, answer = self.random_sample_sentence(sentences_list, PMC_name)\n \n # Return formatted sample\n # Note: question is empty since this is for pretraining with full paper text\n return {\n \"image_dict\": image_dict, # List of images with position information\n \"question\": question, # Empty string for this dataset\n \"answer\": answer, # Full text content\n }\n\n def random_sample_sentence(self, sentences_list, PMC_name):\n \"\"\"\n Sample a segment of sentences from a paper and process inline images\n \n Args:\n sentences_list: List of sentences with image references\n PMC_name: PubMed Central ID for the paper\n \n Returns:\n Tuple of (processed_images, question_text, answer_text)\n \"\"\"\n sentences_length = len(sentences_list)\n \n # Select a segment of the paper - either randomly or around image references\n p = random.random()\n if p >= 0.5:\n # Random segment selection\n if len(sentences_list) > self.sample_sentence_length:\n start = random.randint(0, sentences_length - self.sample_sentence_length)\n sentences_list = sentences_list[start:(start + self.sample_sentence_length)]\n else:\n # Try to select a segment containing images\n if len(sentences_list) > self.sample_sentence_length:\n sample_start = []\n # Find sentences with image references\n for sentence_id in range(len(sentences_list)):\n if sentences_list[sentence_id]['img_ref'] != []:\n # Start 10 sentences before the image if possible\n if sentence_id - 10 < 0:\n sample_start.append(0)\n else:\n if sentence_id - 10 > sentences_length - self.sample_sentence_length:\n sample_start.append(sentences_length - self.sample_sentence_length)\n else:\n sample_start.append(sentence_id - 10)\n \n # If no images found, select random segment\n if sample_start == []:\n start = random.randint(0, sentences_length - self.sample_sentence_length)\n sentences_list = sentences_list[start:(start + self.sample_sentence_length)]\n else:\n # Select a random segment that contains images\n start = sample_start[random.randint(0, len(sample_start) - 1)]\n sentences_list = sentences_list[start:(start + self.sample_sentence_length)]\n \n # Process the selected segment\n text = ''\n images = []\n for ix in sentences_list:\n sentence = ix\n if sentence[\"img_ref\"] == []:\n # Add plain text without images\n text = text + sentence['text']\n else:\n # Stop if we've reached the maximum number of images\n if len(images) + len(sentence[\"img_ref\"]) > self.max_img_size:\n break\n \n # Process each image referenced in the sentence\n for img_id in sentence[\"img_ref\"]:\n img_path = self.img_path + '/' + PMC_name + '_' + img_id + '.jpg'\n if os.path.exists(img_path):\n try:\n # Load and transform the image\n image = Image.open(img_path).convert('RGB') \n image = self.transform(image)\n # Add image with position information\n images.append({'image': image, \"position\": {\"answer\": len(text)}}) \n except:\n # Skip images that can't be loaded\n continue\n # Add the text after processing images\n text = text + sentence['text'] \n \n # For this dataset, we don't use a question-answer format\n # Instead, all text is in the \"answer\" field\n question = ''\n answer = text\n \n return images, question, answer\n\n# Example usage (commented out)\n# csv_path = '/home/cs/leijiayu/wuchaoyi/multi_modal/Data/train_paper.csv' \n# img_path = '/home/cs/leijiayu/data/all_images/figures/'\n# dataset = multi_paper_dataset(csv_path, img_path)\n# print(dataset[0])"
},
{
"path": "src/Dataset/dataset/pmcoa.py",
"content": "# Import necessary libraries for data processing, image handling, and model interaction\nimport csv\nimport json\nimport logging\nimport os\nimport re\nimport difflib\nimport sys\nimport torch\nimport random\nfrom abc import abstractmethod\nfrom itertools import islice\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union\nfrom collections.abc import Mapping\nfrom torch.utils.data import DataLoader\nimport PIL\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom torchvision import transforms\nfrom collections import defaultdict\nfrom PIL import Image\n\nclass PMCOA_Dataset(Dataset):\n \"\"\"\n Dataset for processing scientific figures and captions from PubMed Central Open Access (PMC-OA).\n \n This dataset formulates image captioning as a visual question answering task,\n where the model is prompted with a question about an image and should respond\n with an appropriate caption.\n \n Args:\n csv_path: Path to CSV file with columns [PMC_ID, Figure_path, Caption]\n img_root_dir: Path to image root directory containing figure images\n prompt_json_file: Path to JSON file containing caption prompts\n \n Output:\n Dict: {\n \"image_dict\": [{\"image\": image, \"position\": {\"question\": position}}], \n # image is a tensor of shape [c,w,h,d] [3,512,512,1]\n # position is where to insert the image - either at start (0) or end of question\n \"question\": question, # randomly selected caption prompt\n \"answer\": answer, # original caption from the paper\n }\n \"\"\"\n def __init__(self, csv_path, img_root_dir, prompt_json_file):\n \"\"\"\n Initialize the dataset.\n \n Args:\n csv_path: Path to CSV file with figure metadata\n img_root_dir: Root directory containing figure images\n prompt_json_file: JSON file with caption prompts\n \"\"\"\n self.img_root_dir = img_root_dir\n \n # Load metadata from CSV file\n data_info = pd.read_csv(csv_path)\n self.img_path_list = np.asarray(data_info['Figure_path'])\n self.caption_list = np.asarray(data_info['Caption'])\n \n # Define image transformation pipeline\n # normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n self.transform = transforms.Compose([ \n # Crop and resize images to 512x512, maintaining 80-100% of original content\n transforms.RandomResizedCrop([512, 512], scale=(0.8, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n # Convert to tensor with values in [0, 1]\n transforms.ToTensor(),\n # normalize, # Commented out normalization\n ]) \n\n # Load caption prompts from JSON file\n with open(prompt_json_file, 'r') as f:\n self.caption_prompts = json.load(f)['caption_prompt']\n \n\n def __len__(self):\n \"\"\"Return the total number of samples in the dataset\"\"\"\n return len(self.img_path_list)\n\n def __getitem__(self, index):\n \"\"\"\n Get a single sample from the dataset\n \n Args:\n index: Index of the sample to retrieve\n \n Returns:\n Dictionary containing processed sample with image, question prompt, and caption answer\n \"\"\"\n # Get the image filename and construct full path\n file_name = self.img_path_list[index]\n img_path = os.path.join(self.img_root_dir, file_name)\n \n # Load and preprocess the image\n image = Image.open(img_path).convert('RGB') \n image = self.transform(image) # normalize to [0,1]\n image = image.unsqueeze(-1) # add depth dimension [C, H, W, 1]\n \n # Get the caption and a random prompt\n answer = self.caption_list[index]\n question = random.choice(self.caption_prompts)\n \n # Randomly decide whether to place the image before or after the question\n if random.random() < 0.5:\n # Place image before the question\n image_dict = {\n \"image\": image,\n \"position\": {\n \"question\": 0 # At the beginning of question\n }\n }\n else:\n # Place image after the question\n image_dict = {\n \"image\": image,\n \"position\": {\n \"question\": len(question) # At the end of question\n }\n }\n \n # Return formatted sample\n return {\n \"image_dict\": [image_dict], # List containing one image with position info\n \"question\": question, # Caption prompt\n \"answer\": answer, # Ground truth caption\n }\n \nif __name__ == \"__main__\":\n # Example usage for testing the dataset\n test_dataset = PMCOA_Dataset(\n csv_path='../data_csv/pmcoa_image_caption_train.csv', \n img_root_dir='/home/cs/leijiayu/data/PMCVQA/caption_T060_filtered_top4_sep_v0_subfigures', \n prompt_json_file='./caption_prompt.json'\n )\n \n # Test the first 10 samples\n for i in range(10):\n test_data = test_dataset[i]\n print(test_data['image_dict'][0]['image'].shape) # Should print [3,512,512,1]"
},
{
"path": "src/Dataset/dataset/radiology_feature_prompt.json",
"content": "{\n \"caption_prompt\": [\n \"What disease can be diagnosed from these radiological images and what specific features are typically observed on the images?\",\n \"Identify the disease that is typically associated with these radiological images and describe the classic radiological presentation.\",\n \"Based on the provided images, which disease is most likely to be diagnosed and how does it manifest on radiological examinations?\",\n \"Determine the disease that corresponds to the given radiographic images and describe the characteristic radiological features.\",\n \"With these radiological images, which disease would you suspect and what specific radiographic patterns are typically seen?\",\n \"Analyze the provided images and identify the disease that is commonly associated with such radiological findings. Discuss the characteristic radiographic manifestations.\",\n \"From these radiological images, diagnose the disease and explain the typical radiological presentation observed.\",\n \"Assess the radiographic images and determine the disease that is commonly linked to these findings. Describe the typical radiological features associated with this disease.\",\n \"Examine the provided radiological images and identify the disease that would most likely be diagnosed based on the characteristic radiologic appearance.\",\n \"Based on the presented radiographic findings, indicate the disease that is commonly associated with these images and describe the typical radiological patterns observed.\"\n ]\n}\n"
},
{
"path": "src/Dataset/dataset/radiopaedia.py",
"content": "import csv\nimport json\nimport logging\nimport os\nimport re\nimport difflib\nimport sys\nimport cv2\nimport torch\nimport random\nfrom abc import abstractmethod\nfrom itertools import islice\nfrom scipy import ndimage\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union\nfrom collections.abc import Mapping\nfrom torch.utils.data import DataLoader\nimport PIL\nimport SimpleITK as sitk\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom torchvision import transforms\nfrom collections import defaultdict\nfrom PIL import Image\nimport math\n\nclass Radio_Modality_Dataset(Dataset):\n \"\"\"_summary_\n Args:\n Dataset (_type_): _description_: modality asked task formulated as vqa task for Radiopaedia dataset\n csv_path (_type_): path to csv file\n prompt_json_file (_type_): path to json file containing caption prompts\n Output:\n Dict: {\n \"image_dict\": {\"image\": image, \"position\": {\"question\": 0}}, # image is a tensor of shape [s,c,w,h,d] like, [1,3,512,512,1], position is a dict, random choice of 0 or len(question)\n \"question\": question, # random choice of caption prompts\n \"answer\":answer, # caption\n }\n \"\"\"\n def __init__(self,csv_path,prompt_json_file,modality_json_file,down_sample_ratio = 5):\n data_info = pd.read_csv(csv_path)\n self.down_sample_ratio = down_sample_ratio\n self.img_path_list = np.asarray(data_info['image_path'])\n self.caption_list = np.asarray(data_info['answer'])\n with open(prompt_json_file, 'r') as f:\n self.caption_prompts = json.load(f)['caption_prompt']\n with open(prompt_json_file, 'r') as f:\n self.modality_prompts = json.load(f)['modality_prompt']\n with open(modality_json_file, 'r') as f:\n self.modality_sets = json.load(f)['modality']\n \n def resize_image(self, image):\n if len(image.shape) == 3:\n if image.shape[0] > image.shape[2]:\n image = image.transpose(2,0,1)\n # print('before resize',image.shape)\n image = cv2.resize(image,(512,512),interpolation = cv2.INTER_LINEAR)\n # print('after resize',image.shape)\n image = image[np.newaxis,:,:,:]\n image = np.concatenate([image,image,image],axis=0)\n \n if image.shape[-1] > 64:\n image = ndimage.zoom(image, (3/image.shape[0],512/image.shape[1],512/image.shape[2],64/image.shape[3]), order=0)\n else:\n image = ndimage.zoom(image, (3/image.shape[0],512/image.shape[1],512/image.shape[2],1), order=0)\n return image\n\n def __len__(self):\n return math.ceil(len(self.img_path_list)/self.down_sample_ratio)\n \n def __getitem__(self, index):\n index = (self.down_sample_ratio*index +random.randint(0,self.down_sample_ratio-1))%len(self.img_path_list)\n img_path = self.img_path_list[index]\n try:\n itk_image = sitk.ReadImage(img_path)\n image = sitk.GetArrayFromImage(itk_image)\n image = self.resize_image(image)\n except:\n image = np.random.randn(3,512,512,4)\n \n # image = np.load(img_path) # c,w,h,d\n image = (image-image.min())/(image.max()-image.min())\n contain_nan = (True in np.isnan(image))\n if contain_nan:\n image = np.random.randn(3,512,512,4)\n image = torch.from_numpy(image).float()\n \n if random.random() < 0.5:\n #直接回答\n answer = self.caption_list[index]\n question = random.choice(self.caption_prompts)\n else:\n modality = self.caption_list[index]\n if random.random() < 0.5:\n # 回答为yes\n question = random.choice(self.modality_prompts).replace('modality',modality)\n answer = 'yes'\n else:\n select_modality = modality\n while select_modality == modality:\n select_modality = random.choice(list(self.modality_sets))\n question = random.choice(self.modality_prompts).replace('modality',modality)\n answer = 'no'\n if random.random() < 0.5:\n image_dict = {\n \"image\": image,\n \"position\": {\n \"question\": 0\n }\n }\n else:\n image_dict = {\n \"image\": image,\n \"position\": {\n \"question\": len(question)\n }\n }\n return {\n \"image_dict\": [image_dict],\n \"question\": question,\n \"answer\":answer,\n }\n\nclass RadioVQA_Dataset(Dataset):\n \"\"\"_summary_\n\n Args:\n Dataset (_type_): _description_: caption task formulated as vqa task for Radiopaedia dataset\n csv_path (_type_): path to csv file\n prompt_json_file (_type_): path to json file containing caption prompts\n Output:\n Dict: {\n \"image_dict\": {\"image\": image, \"position\": {\"question\": 0}}, # image is a tensor of shape [s,c,w,h,d] like, [1,3,512,512,1], position is a dict, random choice of 0 or len(question)\n \"question\": question, # random choice of caption prompts\n \"answer\":answer, # caption\n }\n \"\"\"\n def __init__(self,csv_path):\n data_info = pd.read_csv(csv_path)\n # npy_path,image_caption,question,answer\n self.img_path_list = np.asarray(data_info['image_path'])\n self.question_list = np.asarray(data_info['question'])\n self.answer_list = np.asarray(data_info['answer'])\n \n def __len__(self):\n return len(self.img_path_list)\n\n def __getitem__(self, index):\n img_path = self.img_path_list[index]\n image = np.load(img_path)\n \n image = (image-image.min())/(image.max()-image.min())\n contain_nan = (True in np.isnan(image))\n if contain_nan:\n image = np.random.randn(3,512,512,4)\n\n image = torch.from_numpy(image).float()\n answer = self.answer_list[index]\n question = self.question_list[index]\n image_dict = []\n for idx in range(image.shape[0]):\n if random.random() < 0.5:\n dict_idx = {\n \"image\": image[idx],\n \"position\": {\n \"question\": 0\n }\n }\n else:\n dict_idx = {\n \"image\": image[idx],\n \"position\": {\n \"question\": len(question)\n }\n }\n image_dict.append(dict_idx)\n if len(image_dict) > 10:\n images = random.sample(image_dict,10) \n return {\n \"image_dict\": image_dict,\n \"question\": question,\n \"answer\":answer,\n }\n\nclass RadioCaption_Dataset(Dataset):\n def __init__(self,json_path,prompt_json_file):\n with open(json_path, 'r') as file:\n self.json_data = json.load(file)\n with open(prompt_json_file, 'r') as f:\n self.caption_prompts = json.load(f)['caption_prompt']\n\n def __len__(self):\n return len(self.json_data)\n\n def __getitem__(self, index):\n data_index = self.json_data[index]\n patient_pre = data_index['pre']\n patient_pat = data_index['pat']\n img_path = data_index['npy_path']\n finding = data_index['finding']\n impression = data_index['impression']\n prompt_question = random.choice(self.caption_prompts)\n question = patient_pat + ' ' + patient_pre + ' ' + prompt_question\n image = np.load(img_path)\n image = (image-image.min())/(image.max()-image.min())\n contain_nan = (True in np.isnan(image))\n if contain_nan:\n image = np.random.randn(3,512,512,4)\n image = torch.from_numpy(image).float()\n answer = 'Finding: ' + str(finding) + 'Impression: ' + str(impression) \n \n image_dict = []\n for idx in range(image.shape[0]):\n if random.random() < 0.5:\n dict_idx = {\n \"image\": image[idx],\n \"position\": {\n \"question\": 0\n }\n }\n else:\n dict_idx = {\n \"image\": image[idx],\n \"position\": {\n \"question\": len(question)\n }\n }\n image_dict.append(dict_idx)\n return {\n \"image_dict\": image_dict,\n \"question\": question,\n \"answer\":answer,\n } \n\n\nclass Radiofeatures_Dataset(Dataset):\n def __init__(self,json_path,prompt_json_file,disease_prompt_json_file,article_json_file):\n with open(json_path, 'r') as file:\n self.json_data = json.load(file)\n with open(prompt_json_file, 'r') as f:\n self.caption_prompts = json.load(f)['caption_prompt']\n with open(disease_prompt_json_file, 'r') as f:\n self.disease_prompts = json.load(f)['caption_prompt']\n with open(article_json_file, 'r') as f:\n self.article_sets = json.load(f).keys()\n \n def __len__(self):\n return len(self.json_data)\n\n def __getitem__(self, index):\n data_index = self.json_data[index]\n patient_pre = data_index['pre']\n patient_pat = data_index['pat']\n img_path = data_index['npy_path']\n radiographic_features = ' '.join(data_index['radiographic_features'])\n image = np.load(img_path)\n image = (image-image.min())/(image.max()-image.min())\n contain_nan = (True in np.isnan(image))\n if contain_nan:\n image = np.random.randn(3,512,512,4)\n image = torch.from_numpy(image).float()\n \n if random.random() < 0.5:\n articles = ' '.join(data_index['articles'])\n prompt_question = random.choice(self.caption_prompts)\n question = patient_pat + ' ' + patient_pre + ' ' + prompt_question\n answer = articles + 'The Radiographic features can be summarized as follows.' + radiographic_features\n else:\n articles = data_index['title']\n if random.random() < 0.5:\n # 回答为yes\n question = random.choice(self.disease_prompts).replace('disease',articles)\n answer = 'yes'\n else:\n select_articles = articles\n while select_articles == articles:\n select_articles = random.choice(list(self.article_sets))\n question = random.choice(self.disease_prompts).replace('disease',select_articles)\n answer = 'no'\n image_dict = []\n for idx in range(image.shape[0]):\n if random.random() < 0.5:\n dict_idx = {\n \"image\": image[idx],\n \"position\": {\n \"question\": 0\n }\n }\n else:\n dict_idx = {\n \"image\": image[idx],\n \"position\": {\n \"question\": len(question)\n }\n }\n image_dict.append(dict_idx)\n \n return {\n \"image_dict\": image_dict,\n \"question\": question,\n \"answer\":answer,\n } \n\n\"\"\"\nclass Radiodisease_Dataset(Dataset):\n def __init__(self,json_path,prompt_json_file,article_json_file):\n with open(json_path, 'r') as file:\n self.json_data = json.load(file)\n with open(prompt_json_file, 'r') as f:\n self.caption_prompts = json.load(f)['caption_prompt']\n with open(article_json_file, 'r') as f:\n self.article_sets = json.load(f).keys()\n \n def __len__(self):\n return len(self.json_data)\n \n def __getitem__(self, index):\n data_index = self.json_data[index]\n patient_pre = data_index['pre']\n patient_pat = data_index['pat']\n img_path = data_index['npy_path']\n articles = data_index['title']\n if random.random() < 0.5:\n # 回答为yes\n question = random.choice(self.caption_prompts).replace('disease',articles)\n answer = 'yes'\n else:\n select_articles = articles\n while select_articles == articles:\n select_articles = random.choice(list(self.article_sets))\n question = random.choice(self.caption_prompts).replace('disease',select_articles)\n answer = 'no'\n image = np.load(img_path)\n image = (image-image.min())/(image.max()-image.min())\n image = torch.from_numpy(image).float()\n \n image_dict = []\n for idx in range(image.shape[0]):\n if random.random() < 0.5:\n dict_idx = {\n \"image\": image[idx],\n \"position\": {\n \"question\": 0\n }\n }\n else:\n dict_idx = {\n \"image\": image[idx],\n \"position\": {\n \"question\": len(question)\n }\n }\n image_dict.append(dict_idx)\n return {\n \"image_dict\": image_dict,\n \"question\": question,\n \"answer\":answer,\n } \n\n\nclass Radio_modality_binary_Dataset(Dataset):\n def __init__(self,csv_path,prompt_json_file,modality_json_file):\n data_info = pd.read_csv(csv_path)\n self.img_path_list = np.asarray(data_info['image_path'])\n self.caption_list = np.asarray(data_info['answer'])\n with open(prompt_json_file, 'r') as f:\n self.caption_prompts = json.load(f)['modality_prompt']\n with open(modality_json_file, 'r') as f:\n self.modality_sets = json.load(f)['modality']\n \n def resize_image(self, image):\n if len(image.shape) == 3:\n if image.shape[0] > image.shape[2]:\n image = image.transpose(2,0,1)\n # print('before resize',image.shape)\n image = cv2.resize(image,(512,512),interpolation = cv2.INTER_LINEAR)\n # print('after resize',image.shape)\n image = image[np.newaxis,:,:,:]\n image = np.concatenate([image,image,image],axis=0)\n \n if image.shape[-1] > 64:\n image = ndimage.zoom(image, (3/image.shape[0],512/image.shape[1],512/image.shape[2],64/image.shape[3]), order=0)\n else:\n image = ndimage.zoom(image, (3/image.shape[0],512/image.shape[1],512/image.shape[2],1), order=0)\n return image\n\n def __len__(self):\n return len(self.img_path_list)\n \n def __getitem__(self, index):\n img_path = self.img_path_list[index]\n try:\n itk_image = sitk.ReadImage(img_path)\n image = sitk.GetArrayFromImage(itk_image)\n image = self.resize_image(image)\n except:\n image = np.random.randn(3,512,512,4)\n \n image = (image-image.min())/(image.max()-image.min())\n image = torch.from_numpy(image).float()\n modality = self.caption_list[index]\n \n if random.random() < 0.5:\n # 回答为yes\n question = random.choice(self.caption_prompts).replace('modality',modality)\n answer = 'yes'\n else:\n select_modality = modality\n while select_modality == modality:\n select_modality = random.choice(list(self.modality_sets))\n question = random.choice(self.caption_prompts).replace('modality',modality)\n answer = 'no'\n \n if random.random() < 0.5:\n image_dict = {\n \"image\": image,\n \"position\": {\n \"question\": 0\n }\n }\n else:\n image_dict = {\n \"image\": image,\n \"position\": {\n \"question\": len(question)\n }\n }\n return {\n \"image_dict\": [image_dict],\n \"question\": question,\n \"answer\":answer,\n }\n\"\"\""
},
{
"path": "src/Dataset/dataset/report_prompt.json",
"content": "{\n \"caption_prompt\": [\n \"Can you provide a radiology report for this medical image?\",\n \"Describe the medical image you see.\",\n \"What is depicted in this picture?\",\n \"Please report this medical scan.\",\n \"What is the medical significance of this image?\",\n \"What can you infer from this picture?\",\n \"Can you provide a quick summary of this image?\",\n \"Describe this medical scan.\",\n \"Please write a radiology report for this image.\",\n \"Can you summarize the images presented?\",\n \"Please generate a radiology report for this scan.\",\n \"Describe the regions of interest in this scan.\",\n \"Please provide a caption for this medical image.\",\n \"Can you provide a brief summary of this radiograph?\",\n \"Describe the structures involved in this medical image.\",\n \"What are the findings presented in this medical scan?\",\n \"Please write a radiology report for this scan.\",\n \"Can you provide a description of this medical scan?\",\n \"Please caption this medical scan.\",\n \"Can you provide a report summary for this medical scan?\"\n ]\n}"
},
{
"path": "src/Dataset/dataset/spinexr_prompt.json",
"content": "{\n \"caption_prompt\": [\n \"What is the diagnosis for this spine X-ray?\",\n \"Based on this X-ray, what type of spine disease is suspected?\",\n \"Can you identify any abnormality in this spine X-ray?\",\n \"What are the findings in this spine X-ray?\",\n \"What pathology is indicated by this spine X-ray?\",\n \"What lung disease is likely present in this spine X-ray?\",\n \"What are the potential causes of the findings in this spine X-ray?\",\n \"What are your conclusions from this spine X-ray?\",\n \"What is your interpretation of this spine X-ray?\",\n \"What abnormalities are present in this spine X-ray?\",\n \"What is the differential diagnosis for the findings in this spine X-ray?\"\n ]\n}"
},
{
"path": "src/Dataset/dataset/vqa.py",
"content": "import csv\nimport json\nimport logging\nimport os\nimport re\nimport difflib\nimport sys\nimport torch\nimport random\nfrom abc import abstractmethod\nfrom itertools import islice\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union\nfrom collections.abc import Mapping\nfrom torch.utils.data import DataLoader\nimport PIL\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom torchvision import transforms\nfrom collections import defaultdict\nfrom PIL import Image\n\nclass VQA_Dataset(Dataset):\n \"\"\"_summary_\n Args:\n Dataset (_type_): \n csv_path (_type_): path to csv file\n Output:\n Dict: {\n \"image_dict\": {\"image\": image, \"position\": {\"question\": 0}}, # image is a tensor of shape [c,w,h,d] [3,512,512,1], position is a dict, random choice of 0 or len(question)\n \"question\": question, # random choice of caption prompts\n \"answer\":answer, # caption\n }\n \"\"\"\n def __init__(self,csv_path):\n data_info = pd.read_csv(csv_path)\n self.img_root_dir_list = np.asarray(data_info['img_root_dir'])\n self.img_path_list = np.asarray(data_info['Figure_path'])\n self.question_list = np.asarray(data_info['Question'])\n self.answer_list = np.asarray(data_info['Answer'])\n # PMC_ID,Figure_path,Question,Answer\n self.transform = transforms.Compose([ \n transforms.RandomResizedCrop([512,512],scale=(0.8, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n transforms.ToTensor(),\n ]) \n\n\n def __len__(self):\n return len(self.img_path_list)\n\n def __getitem__(self, index):\n file_name = self.img_path_list[index]\n img_root_dir = self.img_root_dir_list[index]\n img_path = os.path.join(img_root_dir,file_name)\n image = Image.open(img_path).convert('RGB') \n image = self.transform(image)\n image = image.unsqueeze(-1)\n answer = self.answer_list[index]\n question = str(self.question_list[index])\n if random.random() < 0.5:\n image_dict = {\n \"image\": image,\n \"position\": {\n \"question\": 0\n }\n }\n else:\n image_dict = {\n \"image\": image,\n \"position\": {\n \"question\": len(question)\n }\n }\n return {\n \"image_dict\": [image_dict],\n \"question\": question,\n \"answer\":answer,\n }\n \nif __name__ == \"__main__\":\n test_dataset = PMCVQA_Dataset(csv_path = '../data_csv/pmcvqa_train.csv')\n for i in range(10):\n test_data = test_dataset[i]\n print(test_data['image_dict'][0]['image'].shape) # [3,512,512,1]\n \n \n\n\n\n"
},
{
"path": "src/Dataset/dataset/yes_no_prompt.json",
"content": "{\n \"caption_prompt\": [\n \"Is the disease visible in the image?\",\n \"Does the image show signs of disease?\",\n \"Does the image show any disease?\",\n \"Is there any disease in the affected area?\",\n \"Does the image depict any visible disease?\",\n \"Is there an presence of disease in the image?\",\n \"Are there any visible signs of disease in the image?\",\n \"Does the image exhibit any disease?\",\n \"Are there disease visible in the image?\",\n \"Does the image show any signs of disease?\",\n \"Can you identify any visible signs of disease in the image?\",\n \"Is there any indication of disease in the image?\",\n \"Does the image show signs of disease?\",\n \"Does the image show any visible signs of disease?\"\n ]\n}\n\n\n"
},
{
"path": "src/Dataset/multi_dataset.py",
"content": "# Import necessary libraries for data processing and model interaction\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport transformers\nimport pandas as pd\nimport copy \nimport random \nimport os\nimport numpy as np\nimport tqdm\nimport torch\nimport json\nfrom PIL import Image\nimport math\nimport torchvision\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer\nfrom .dataset import *\nimport spacy\nfrom spacy.tokens import Span\nfrom scispacy.abbreviation import AbbreviationDetector\nfrom scispacy.umls_linking import UmlsEntityLinker\n\nclass umls_extractor:\n \"\"\"\n Extracts medical entities from text using scispacy and UMLS\n \"\"\"\n def __init__(self):\n \"\"\"Initialize the NLP pipeline with medical entity linking capabilities\"\"\"\n nlp = spacy.load(\"en_core_sci_lg\")\n nlp.add_pipe(\"abbreviation_detector\")\n nlp.add_pipe(\"scispacy_linker\", config={\"resolve_abbreviations\": True, \"linker_name\": \"umls\"})\n self.nlp = nlp\n \n def extract(self, text):\n \"\"\"\n Extract medical entities from text\n \n Args:\n text: Input text to process\n \n Returns:\n Set of extracted entities\n \"\"\"\n doc = self.nlp(text)\n ent_set = doc.ents\n return ent_set\n\ndef find_position(label, key_embeddings):\n \"\"\"\n Creates a tensor of weights for loss calculation based on important keywords\n \n Args:\n label: Token IDs tensor for the target sequence\n key_embeddings: List of token ID tensors for important medical terms\n \n Returns:\n Tensor of weights where important terms get higher weight (3)\n \"\"\"\n loss_reweight = torch.ones(label.shape)\n for i in range(len(label)):\n if label[i] == -100: # Skip padding or ignored tokens\n loss_reweight[i] = 0\n else:\n for key_embedding in key_embeddings:\n if torch.equal(label[i:i+len(key_embedding)], key_embedding):\n loss_reweight[i:i+len(key_embedding)] = 3 # Increase weight for important terms\n return loss_reweight\n\ndef stack_images(images):\n \"\"\"\n Processes and stacks a list of images to create a batch\n with consistent dimensions\n \n Args:\n images: List of image tensors\n \n Returns:\n Stacked and resized tensor of images\n \"\"\"\n target_H = 512\n target_W = 512\n target_D = 4\n if len(images) == 0:\n return torch.zeros((1, 3, target_H, target_W, target_D))\n \n MAX_D = 4\n D_list = list(range(4, 65, 4))\n \n # Find maximum depth among all images\n for ii in images:\n try:\n D = ii.shape[3]\n if D > MAX_D:\n MAX_D = D\n except:\n continue\n \n # Select optimal target depth\n for temp_D in D_list:\n if abs(temp_D - MAX_D) < abs(target_D - MAX_D):\n target_D = temp_D\n \n # Resize and stack all images\n stack_images = []\n for s in images:\n if len(s.shape) == 3:\n stack_images.append(torch.nn.functional.interpolate(s.unsqueeze(0).unsqueeze(-1), size=(target_H, target_W, target_D)))\n else:\n stack_images.append(torch.nn.functional.interpolate(s.unsqueeze(0), size=(target_H, target_W, target_D)))\n \n images = torch.cat(stack_images, dim=0)\n return images\n\n\nclass multi_dataset(Dataset):\n \"\"\"\n A dataset class that combines multiple medical imaging datasets\n for training a multimodal model\n \"\"\"\n def __init__(self, text_tokenizer, max_seq=2048, max_img_size=100, image_num=32, voc_size=32000):\n \"\"\"\n Initialize the multimodal dataset\n \n Args:\n text_tokenizer: Tokenizer or path to tokenizer\n max_seq: Maximum sequence length\n max_img_size: Maximum number of images to process\n image_num: Number of image tokens per image\n voc_size: Vocabulary size for the tokenizer\n \"\"\"\n self.text_tokenizer = text_tokenizer\n self.max_img_size = max_img_size\n self.image_num = image_num\n self.max_seq = max_seq\n self.voc_size = voc_size\n self.H = 512\n self.W = 512\n self.image_padding_tokens = []\n self.words_extract = umls_extractor()\n \n # Initialize tokenizer if path is provided\n if isinstance(self.text_tokenizer, str):\n self.text_tokenizer = LlamaTokenizer.from_pretrained(\n self.text_tokenizer,\n )\n \n # Add special tokens for image references\n special_token = {\"additional_special_tokens\": [\"\", \"\"]}\n for i in range(max_img_size):\n image_padding_token = \"\"\n for j in range(image_num):\n image_token = \"\"\n image_padding_token = image_padding_token + image_token\n special_token[\"additional_special_tokens\"].append(\"\")\n self.image_padding_tokens.append(image_padding_token)\n \n self.text_tokenizer.add_special_tokens(\n special_token\n )\n self.text_tokenizer.pad_token_id = 0\n self.text_tokenizer.bos_token_id = 1\n self.text_tokenizer.eos_token_id = 2\n\n # Initialize empty lists for dataset tracking\n self.data_whole_2D = []\n self.data_whole_3D = []\n self.dataset_reflect = {}\n \n ### 2D datasets\n ### Pretrain datasets - currently commented out\n # paper_inline_dataset = Paper_Inline_dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/multi_modal/Data/paper_train.csv', \n # img_path = '/home/cs/leijiayu/data/all_images/figures/')\n # self.dataset_reflect['paper_inline_dataset'] = paper_inline_dataset\n # self.data_whole_2D = self.data_whole_2D + [{'paper_inline_dataset':i} for i in range(len(paper_inline_dataset))]\n # print('paper_inline_dataset loaded')\n \n # pmcoa_dataset = PMCOA_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/pmcoa_image_caption_train.csv', \n # img_root_dir = '/home/cs/leijiayu/data/PMCVQA/caption_T060_filtered_top4_sep_v0_subfigures', \n # prompt_json_file = '/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/caption_prompt.json')\n # self.dataset_reflect['pmcoa_dataset'] = pmcoa_dataset\n # self.data_whole_2D = self.data_whole_2D + [{'pmcoa_dataset':i} for i in range(len(pmcoa_dataset))]\n # print('pmcoa_dataset loaded')\n \n ### SFT (Supervised Fine-Tuning) datasets\n ### MedPix datasets\n medpix_multi_dataset = MedPix_Multi_Dataset(csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/MedPix_multi_train.csv')\n self.dataset_reflect['medpix_multi_dataset'] = medpix_multi_dataset\n self.data_whole_2D = self.data_whole_2D + [{'medpix_multi_dataset': i} for i in range(len(medpix_multi_dataset))]\n print('medpix_multi_dataset loaded')\n \n medpix_single_dataset = MedPix_Single_Dataset(csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/MedPix_single_train.csv')\n self.dataset_reflect['medpix_single_dataset'] = medpix_single_dataset\n self.data_whole_2D = self.data_whole_2D + [{'medpix_single_dataset': i} for i in range(len(medpix_single_dataset))]\n print('medpix_single_dataset loaded')\n \n medpix_qa_dataset = MedPix_QA_Dataset(csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/MedPix_questions_train.csv')\n self.dataset_reflect['medpix_qa_dataset'] = medpix_qa_dataset\n self.data_whole_2D = self.data_whole_2D + [{'medpix_qa_dataset': i} for i in range(len(medpix_qa_dataset))]\n print('medpix_qa_dataset loaded')\n \n ### Chest X-ray datasets\n ### Caption datasets\n chestxray_caption_dataset = ChestXray_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/mimic_caption_train.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/report_prompt.json'\n )\n self.dataset_reflect['chestxray_caption_dataset'] = chestxray_caption_dataset\n self.data_whole_2D = self.data_whole_2D + [{'chestxray_caption_dataset': i} for i in range(len(chestxray_caption_dataset))]\n print('chestxray_caption_dataset loaded')\n \n ### Binary classification datasets\n chestxray_dataset_bn = Binary_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/chestxray_balance_train_new.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json'\n )\n self.dataset_reflect['chestxray_dataset_bn'] = chestxray_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'chestxray_dataset_bn': i} for i in range(len(chestxray_dataset_bn))]\n print('chestxray_dataset_bn loaded')\n \n pcxr_dataset_bn = Binary_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/pcxr_balance_train.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json'\n )\n self.dataset_reflect['pcxr_dataset_bn'] = pcxr_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'pcxr_dataset_bn': i} for i in range(len(pcxr_dataset_bn))]\n print('pcxr_dataset_bn loaded')\n \n mammo_dataset_bn = Binary_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/mammo_balance_train.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json'\n )\n self.dataset_reflect['mammo_dataset_bn'] = mammo_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'mammo_dataset_bn': i} for i in range(len(mammo_dataset_bn))]\n print('mammo_dataset_bn loaded')\n \n spinexr_dataset_bn = Binary_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/spinexr_balance_train.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json'\n )\n self.dataset_reflect['spinexr_dataset_bn'] = spinexr_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'spinexr_dataset_bn': i} for i in range(len(spinexr_dataset_bn))]\n print('spinexr_dataset_bn loaded')\n \n ### Multi-label classification datasets\n chestxray_dataset = ChestXray_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/chestxray_new.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/cls_prompt.json'\n )\n self.dataset_reflect['chestxray_dataset'] = chestxray_dataset\n self.data_whole_2D = self.data_whole_2D + [{'chestxray_dataset': i} for i in range(len(chestxray_dataset))]\n print('chestxray_dataset loaded')\n \n pcxr_dataset = ChestXray_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/pcxr_train_new.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/cls_prompt.json'\n )\n self.dataset_reflect['pcxr_dataset'] = pcxr_dataset\n self.data_whole_2D = self.data_whole_2D + [{'pcxr_dataset': i} for i in range(len(pcxr_dataset))]\n print('pcxr_dataset loaded')\n \n mammo_dataset = ChestXray_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/mammo_train_new.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/mammo_prompt.json'\n )\n self.dataset_reflect['mammo_dataset'] = mammo_dataset\n self.data_whole_2D = self.data_whole_2D + [{'mammo_dataset': i} for i in range(len(mammo_dataset))]\n print('mammo_dataset loaded')\n \n spinexr_dataset = ChestXray_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/spinexr_train_new.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/spinexr_prompt.json'\n )\n self.dataset_reflect['spinexr_dataset'] = spinexr_dataset\n self.data_whole_2D = self.data_whole_2D + [{'spinexr_dataset': i} for i in range(len(spinexr_dataset))]\n print('spinexr_dataset loaded')\n \n ### VQA (Visual Question Answering) datasets\n pmcvqa_dataset = VQA_Dataset(csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/pmcvqa_train.csv')\n self.dataset_reflect['pmcvqa_dataset'] = pmcvqa_dataset\n self.data_whole_2D = self.data_whole_2D + [{'pmcvqa_dataset': i} for i in range(len(pmcvqa_dataset))]\n print('pmcvqa_dataset loaded')\n \n casereport_dataset = CaseReport_dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/filtered_case_report_train.csv', \n img_path='/home/cs/leijiayu/data/all_images/figures/'\n )\n self.dataset_reflect['casereport_dataset'] = casereport_dataset\n self.data_whole_2D = self.data_whole_2D + [{'casereport_dataset': i} for i in range(len(casereport_dataset))]\n print('casereport_dataset loaded')\n \n vqarad_dataset = VQA_Dataset(csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/vqarad_train.csv')\n self.dataset_reflect['vqarad_dataset'] = vqarad_dataset\n self.data_whole_2D = self.data_whole_2D + [{'vqarad_dataset': i} for i in range(len(vqarad_dataset))]\n print('vqarad_dataset loaded')\n \n slake_dataset = VQA_Dataset(csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/slakevqa_train.csv')\n self.dataset_reflect['slake_dataset'] = slake_dataset\n self.data_whole_2D = self.data_whole_2D + [{'slake_dataset': i} for i in range(len(slake_dataset))]\n print('slake_dataset loaded')\n \n ### 3D datasets\n radiovqa_dataset = RadioVQA_Dataset(csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/radiology_vqa_train.csv')\n self.dataset_reflect['radiovqa_dataset'] = radiovqa_dataset\n self.data_whole_3D = self.data_whole_3D + [{'radiovqa_dataset': i} for i in range(len(radiovqa_dataset))]\n print('radiovqa_dataset loaded')\n \n radiomodality_dataset = Radio_Modality_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/radio_modality_train.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/modality_prompt.json',\n modality_json_file='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/modality_set.json'\n )\n self.dataset_reflect['radiomodality_dataset'] = radiomodality_dataset\n self.data_whole_3D = self.data_whole_3D + [{'radiomodality_dataset': i} for i in range(len(radiomodality_dataset))]\n print('radiomodality_dataset loaded')\n \n radiocaption_dataset = RadioCaption_Dataset(\n json_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/radiology_article_npy_train.json',\n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/caption_prompt.json',\n )\n self.dataset_reflect['radiocaption_dataset'] = radiocaption_dataset\n self.data_whole_3D = self.data_whole_3D + [{'radiocaption_dataset': i} for i in range(len(radiocaption_dataset))]\n print('radiocaption_dataset loaded')\n \n radiofeatures_dataset = Radiofeatures_Dataset(\n json_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/radiology_article_npy_train.json',\n prompt_json_file='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/radiology_feature_prompt.json',\n disease_prompt_json_file='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json',\n article_json_file='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/articles_resave.json'\n )\n self.dataset_reflect['radiofeatures_dataset'] = radiofeatures_dataset\n self.data_whole_3D = self.data_whole_3D + [{'radiofeatures_dataset': i} for i in range(len(radiofeatures_dataset))]\n print('radiofeatures_dataset loaded')\n \n # Combine all datasets\n self.data_whole = self.data_whole_2D + self.data_whole_3D\n \n \n def __len__(self):\n \"\"\"Return the total number of samples in the combined dataset\"\"\"\n return len(self.data_whole)\n \n def __getitem__(self, idx):\n \"\"\"\n Get a specific sample from the dataset\n \n Args:\n idx: Index of the sample to retrieve\n \n Returns:\n Dictionary containing processed inputs for model training\n \"\"\"\n # Get sample from the appropriate dataset\n sample = list(self.data_whole[idx].items())[0]\n dataset_index = sample[0]\n sample = self.dataset_reflect[sample[0]][sample[1]] \n '''\n Dict: {\n \"image_dict\": [\n {\"image\": image, # image is a tensor of shape [c,w,h,d], c is channel=3, w is width, h is height, d is depth(1 for chestxray,pmcoa,pmcvqa)\n \"position\": {\"question\": 0}}, position is a dict, random choice of 0 or len(question)\n ]\n \"question\": question, \n \"answer\":answer, \n }\n '''\n images = sample[\"image_dict\"]\n question = sample[\"question\"]\n answer = sample[\"answer\"]\n \n # Process text and images together\n images, question, answer = self.text_add_image(images, question, answer)\n \n # Stack images into a single tensor\n try:\n vision_x = stack_images(images)\n except:\n print(self.data_whole[idx].items())\n \n # Tokenize combined question and answer text\n self.text_tokenizer.padding_side = \"right\"\n text_tensor = self.text_tokenizer(\n question + ' ' + answer, max_length=self.max_seq, truncation=True, padding=\"max_length\", return_tensors=\"pt\"\n )\n lang_x = text_tensor[\"input_ids\"][0]\n attention_mask = text_tensor[\"attention_mask\"][0]\n \n # Add EOS token if necessary\n try:\n lang_x[torch.sum(attention_mask)] = self.text_tokenizer.eos_token_id\n except:\n pass\n \n # Extract important medical terms for loss weighting\n emphasize_words = []\n emphasize_words = [str(_) for _ in self.words_extract.extract(answer)]\n \n if emphasize_words != []:\n emphasize_words_tensor = self.text_tokenizer(\n emphasize_words, max_length=self.max_seq\n )\n key_embeddings = [torch.tensor(_[1:]) for _ in emphasize_words_tensor['input_ids']]\n else:\n key_embeddings = []\n \n # Tokenize question to determine its length\n question_tensor = self.text_tokenizer(\n question, max_length=self.max_seq, truncation=True, padding=\"max_length\", return_tensors=\"pt\"\n )\n question_length = torch.sum(question_tensor[\"attention_mask\"][0])\n \n # Create labels for training (ignore question tokens, padding, and special tokens)\n labels = lang_x.clone()\n labels[labels == self.text_tokenizer.pad_token_id] = -100\n labels[labels >= self.voc_size] = -100\n labels[:question_length] = -100\n \n # Apply weights to important medical terms in the labels\n reweight_tensor = find_position(labels, key_embeddings)\n \n # Don't use keyword emphasis for certain datasets\n if dataset_index == 'paper_inline_dataset':\n emphasize_words = []\n \n # Return all processed inputs for model training\n return {\n 'vision_x': vision_x,\n 'lang_x': lang_x, \n 'attention_mask': attention_mask, \n 'labels': labels, \n 'loss_reweight': reweight_tensor, \n 'key_words_query': emphasize_words\n }\n \n def text_add_image(self, images, question, answer):\n \"\"\"\n Insert image tokens into text at appropriate positions\n \n Args:\n images: List of image tensors\n question: Question text\n answer: Answer text\n \n Returns:\n Tuple of (processed_images, question_with_image_tokens, answer_with_image_tokens)\n \"\"\"\n ref_image = []\n question = str(question)\n answer = str(answer)\n \n # Initialize lists to track image insertion positions\n question_list = [[] for _ in range(len(str(question)))]\n answer_list = [[] for _ in range(len(str(answer)))]\n \n # Process each image and its position\n for index, image in enumerate(images):\n ref_image.append(image[\"image\"])\n position = image[\"position\"]\n position = list(position.items())[0]\n \n # Store image indices at their insertion positions\n if position[0] == 'question':\n insert_loc = position[1] - 1\n if insert_loc < 0:\n insert_loc = 0\n question_list[insert_loc].append(index)\n if position[0] == 'answer':\n insert_loc = position[1] - 1\n if insert_loc < 0:\n insert_loc = 0\n answer_list[insert_loc].append(index)\n \n # Build new question text with image tokens\n new_question = ''\n new_answer = ''\n question = str(question)\n for char_i in range(len(question)):\n if question_list[char_i] == []:\n new_question = new_question + question[char_i]\n if question_list[char_i] != []:\n for img_index in question_list[char_i]:\n try:\n new_question = new_question + '' + self.image_padding_tokens[img_index] + ''\n except:\n print(\"Error: out of max image input size\")\n new_question = new_question + question[char_i]\n \n # Build new answer text with image tokens\n answer = str(answer)\n for char_i in range(len(str(answer))):\n if answer_list[char_i] == []:\n new_answer = new_answer + answer[char_i]\n if answer_list[char_i] != []:\n for img_index in answer_list[char_i]:\n try:\n new_answer = new_answer + '' + self.image_padding_tokens[img_index] + ''\n except:\n print(\"Error: out of max image input size\")\n new_answer = new_answer + answer[char_i]\n \n # Remove bullet points for cleaner text\n new_answer = new_answer.replace('•', '')\n return ref_image, new_question, new_answer\n \n \n \n \n \n \n# torch.set_printoptions(profile=\"full\") \n# text_tokenizer = '/home/cs/leijiayu/wuchaoyi/Finetune_LLAMA/LLAMA_Model/tokenizer'\n# dataset = multi_dataset(text_tokenizer = text_tokenizer)\n# print(len(dataset))\n# for i in range(10):\n# dataset[i]\n# input()"
},
{
"path": "src/Dataset/multi_dataset_test.py",
"content": "# Import necessary libraries for data processing, model interaction, and image handling\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport transformers\nimport pandas as pd\nimport copy \nimport random \nimport os\nimport numpy as np\nimport tqdm\nimport torch\nimport json\nfrom PIL import Image\nimport math\nimport torchvision\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer\nfrom .dataset import *\n\n\ndef stack_images(images):\n \"\"\"\n Process and stack multiple images to a consistent format\n \n Args:\n images: List of image tensors\n \n Returns:\n Stacked tensor of images with consistent dimensions\n \"\"\"\n # Set target dimensions\n target_H = 512\n target_W = 512\n target_D = 4\n \n # Return empty tensor if no images\n if len(images) == 0:\n return torch.zeros((1, 3, target_H, target_W, target_D))\n \n # Find optimal depth for 3D images\n MAX_D = 4\n D_list = list(range(4, 65, 4))\n \n for ii in images:\n try:\n D = ii.shape[3]\n if D > MAX_D:\n MAX_D = D\n except:\n continue\n \n # Select closest target depth\n for temp_D in D_list:\n if abs(temp_D - MAX_D) < abs(target_D - MAX_D):\n target_D = temp_D\n \n # Process each image to the target dimensions\n stack_images = []\n for s in images:\n s = torch.tensor(s)\n if len(s.shape) == 3:\n # For 2D images, add depth dimension\n stack_images.append(torch.nn.functional.interpolate(s.unsqueeze(0).unsqueeze(-1), size=(target_H, target_W, target_D)))\n else:\n # For 3D images\n stack_images.append(torch.nn.functional.interpolate(s.unsqueeze(0), size=(target_H, target_W, target_D)))\n \n # Concatenate all processed images\n images = torch.cat(stack_images, dim=0)\n return images\n\nclass multi_dataset(Dataset):\n \"\"\"\n Dataset class for testing multimodal models on different medical imaging tasks\n \"\"\"\n def __init__(self, text_tokenizer, test_split='close', max_seq=2048, max_img_size=10, image_num=32, voc_size=32000):\n \"\"\"\n Initialize the test dataset\n \n Args:\n text_tokenizer: Tokenizer or path to tokenizer\n test_split: Which test split to use ('diagnosis', 'modality', 'vqa', 'caption', 'feature')\n max_seq: Maximum sequence length\n max_img_size: Maximum number of images to process\n image_num: Number of image tokens per image\n voc_size: Vocabulary size\n \"\"\"\n self.text_tokenizer = text_tokenizer\n self.max_img_size = max_img_size\n self.image_num = image_num\n self.max_seq = max_seq\n self.voc_size = voc_size\n self.H = 512\n self.W = 512\n self.image_padding_tokens = []\n self.test_split = test_split\n \n # Initialize tokenizer with special tokens for images\n if isinstance(self.text_tokenizer, str):\n self.text_tokenizer = LlamaTokenizer.from_pretrained(\n self.text_tokenizer,\n )\n special_token = {\"additional_special_tokens\": [\"\", \"\"]}\n \n # Create special tokens for each image position and patch\n for i in range(max_img_size):\n image_padding_token = \"\"\n for j in range(image_num):\n image_token = \"\"\n image_padding_token = image_padding_token + image_token\n special_token[\"additional_special_tokens\"].append(\"\")\n self.image_padding_tokens.append(image_padding_token)\n \n # Add special tokens to tokenizer\n self.text_tokenizer.add_special_tokens(\n special_token\n )\n # Set standard special tokens for LLaMA\n self.text_tokenizer.pad_token_id = 0\n self.text_tokenizer.bos_token_id = 1\n self.text_tokenizer.eos_token_id = 2\n\n # Initialize dataset tracking variables\n self.data_whole_2D = []\n self.data_whole_3D = []\n self.dataset_reflect = {}\n \n ### Load appropriate datasets based on test_split ###\n \n # Diagnosis task datasets (binary and multi-class classification)\n if self.test_split == 'diagnosis':\n # Binary classification datasets\n chestxray_dataset_bn = Binary_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/chestxray_balance_test.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json'\n )\n self.dataset_reflect['chestxray_dataset_bn'] = chestxray_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'chestxray_dataset_bn': i} for i in range(len(chestxray_dataset_bn))]\n print('chestxray_dataset_bn loaded')\n \n pcxr_dataset_bn = Binary_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/pcxr_balance_test.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json'\n )\n self.dataset_reflect['pcxr_dataset_bn'] = pcxr_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'pcxr_dataset_bn': i} for i in range(len(pcxr_dataset_bn))]\n print('pcxr_dataset_bn loaded')\n \n mammo_dataset_bn = Binary_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/mammo_balance_test.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json'\n )\n self.dataset_reflect['mammo_dataset_bn'] = mammo_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'mammo_dataset_bn': i} for i in range(len(mammo_dataset_bn))]\n print('mammo_dataset_bn loaded')\n \n spinexr_dataset_bn = Binary_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/spinexr_balance_test.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json'\n )\n self.dataset_reflect['spinexr_dataset_bn'] = spinexr_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'spinexr_dataset_bn': i} for i in range(len(spinexr_dataset_bn))]\n print('spinexr_dataset_bn loaded')\n \n # Multi-label classification datasets\n chestxray_dataset = ChestXray_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/chestxray_test.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/cls_prompt.json'\n )\n self.dataset_reflect['chestxray_dataset'] = chestxray_dataset\n self.data_whole_2D = self.data_whole_2D + [{'chestxray_dataset': i} for i in range(len(chestxray_dataset))]\n print('chestxray_dataset loaded')\n \n pcxr_dataset = ChestXray_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/pcxr_test.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/cls_prompt.json'\n )\n self.dataset_reflect['pcxr_dataset'] = pcxr_dataset\n self.data_whole_2D = self.data_whole_2D + [{'pcxr_dataset': i} for i in range(len(pcxr_dataset))]\n print('pcxr_dataset loaded')\n \n mammo_dataset = ChestXray_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/mammo_test.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/mammo_prompt.json'\n )\n self.dataset_reflect['mammo_dataset'] = mammo_dataset\n self.data_whole_2D = self.data_whole_2D + [{'mammo_dataset': i} for i in range(len(mammo_dataset))]\n print('mammo_dataset loaded')\n \n spinexr_dataset = ChestXray_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/spinexr_test.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/spinexr_prompt.json'\n )\n self.dataset_reflect['spinexr_dataset'] = spinexr_dataset\n self.data_whole_2D = self.data_whole_2D + [{'spinexr_dataset': i} for i in range(len(spinexr_dataset))]\n print('spinexr_dataset loaded')\n \n # Modality classification datasets\n if self.test_split == 'modality':\n radiomodality_dataset = Radio_Modality_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/radio_modality_test.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/modality_prompt.json',\n modality_json_file='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/modality_set.json',\n down_sample_ratio=1\n )\n self.dataset_reflect['radiomodality_dataset'] = radiomodality_dataset\n self.data_whole_3D = self.data_whole_3D + [{'radiomodality_dataset': i} for i in range(len(radiomodality_dataset))]\n print('radiomodality_dataset loaded')\n \n # Visual Question Answering datasets\n if self.test_split == 'vqa':\n # Load VQA datasets for 2D images\n pmcvqa_dataset = VQA_Dataset(csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/pmcvqa_test.csv')\n self.dataset_reflect['pmcvqa_dataset'] = pmcvqa_dataset\n self.data_whole_2D = self.data_whole_2D + [{'pmcvqa_dataset': i} for i in range(len(pmcvqa_dataset))]\n print('pmcvqa_dataset loaded')\n \n casereport_dataset = CaseReport_dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/filtered_case_report_test.csv', \n img_path='/home/cs/leijiayu/data/all_images/figures/'\n )\n self.dataset_reflect['casereport_dataset'] = casereport_dataset\n self.data_whole_2D = self.data_whole_2D + [{'casereport_dataset': i} for i in range(len(casereport_dataset))]\n print('casereport_dataset loaded')\n \n vqarad_dataset = VQA_Dataset(csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/vqarad_test.csv')\n self.dataset_reflect['vqarad_dataset'] = vqarad_dataset\n self.data_whole_2D = self.data_whole_2D + [{'vqarad_dataset': i} for i in range(len(vqarad_dataset))]\n print('vqarad_dataset loaded')\n \n slake_dataset = VQA_Dataset(csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/slakevqa_test.csv')\n self.dataset_reflect['slake_dataset'] = slake_dataset\n self.data_whole_2D = self.data_whole_2D + [{'slake_dataset': i} for i in range(len(slake_dataset))]\n print('slake_dataset loaded')\n \n # Load VQA dataset for 3D images\n radiovqa_dataset = RadioVQA_Dataset(csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/radiology_vqa_test.csv')\n self.dataset_reflect['radiovqa_dataset'] = radiovqa_dataset\n self.data_whole_3D = self.data_whole_3D + [{'radiovqa_dataset': i} for i in range(len(radiovqa_dataset))]\n print('radiovqa_dataset loaded')\n \n # Image captioning datasets\n if self.test_split == 'caption':\n # Load captioning datasets for 2D images\n chestxray_caption_dataset = ChestXray_Dataset(\n csv_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/mimic_caption_test.csv', \n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/report_prompt.json'\n )\n self.dataset_reflect['chestxray_caption_dataset'] = chestxray_caption_dataset\n self.data_whole_2D = self.data_whole_2D + [{'chestxray_caption_dataset': i} for i in range(len(chestxray_caption_dataset))]\n print('chestxray_caption_dataset loaded')\n \n # Load captioning dataset for 3D images\n radiocaption_dataset = RadioCaption_Dataset(\n json_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/radiology_article_npy_test.json',\n prompt_json_file='/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/caption_prompt.json'\n )\n self.dataset_reflect['radiocaption_dataset'] = radiocaption_dataset\n self.data_whole_3D = self.data_whole_3D + [{'radiocaption_dataset': i} for i in range(len(radiocaption_dataset))]\n print('radiocaption_dataset loaded')\n \n # Feature extraction datasets\n if self.test_split == 'feature': \n radiofeatures_dataset = Radiofeatures_Dataset(\n json_path='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/radiology_article_npy_test.json',\n prompt_json_file='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/radiology_feature_prompt.json',\n disease_prompt_json_file='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json',\n article_json_file='/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/articles_resave.json'\n )\n self.dataset_reflect['radiofeatures_dataset'] = radiofeatures_dataset\n self.data_whole_3D = self.data_whole_3D + [{'radiofeatures_dataset': i} for i in range(len(radiofeatures_dataset))]\n print('radiofeatures_dataset loaded')\n \n # Combine 2D and 3D datasets\n self.data_whole = self.data_whole_2D + self.data_whole_3D\n \n \n def __len__(self):\n \"\"\"Return the total number of samples across all test datasets\"\"\"\n return len(self.data_whole)\n \n def __getitem__(self, idx):\n \"\"\"\n Get a single test sample\n \n Args:\n idx: Index of the sample\n \n Returns:\n Dictionary with processed inputs for model testing\n \"\"\"\n # Extract dataset name and sample index\n sample = list(self.data_whole[idx].items())[0]\n belong_to = sample[0] # Which dataset this sample belongs to\n sample = self.dataset_reflect[sample[0]][sample[1]] # Get the actual sample\n \n '''\n Dict: {\n \"image_dict\": [\n {\"image\": image, # image is a tensor of shape [c,w,h,d], c is channel=3, w is width, h is height, d is depth(1 for chestxray,pmcoa,pmcvqa)\n \"position\": {\"question\": 0}}, position is a dict, random choice of 0 or len(question)\n ]\n \"question\": question, \n \"answer\":answer, \n }\n '''\n # Extract images and text from sample\n images = sample[\"image_dict\"]\n # Limit number of images to prevent memory issues\n if len(images) > 8:\n images = random.sample(images, 8)\n \n question = str(sample[\"question\"])\n answer = str(sample[\"answer\"])\n \n # Combine text and images\n images, question, answer = self.text_add_image(images, question, answer)\n \n # Create stacked image tensor\n try:\n vision_x = stack_images(images)\n except:\n print(self.data_whole[idx].items())\n input()\n\n # Return processed sample for testing\n return {\n 'vision_x': vision_x,\n 'question': question, \n 'answer': answer, \n 'belong_to': belong_to,\n }\n \n def text_add_image(self, images, question, answer):\n \"\"\"\n Insert image tokens into the text at appropriate positions\n \n Args:\n images: List of image tensors\n question: Question text\n answer: Answer text\n \n Returns:\n Tuple of (processed_images, question_with_image_tokens, answer_with_image_tokens)\n \"\"\"\n ref_image = []\n # Initialize lists to track image insertion positions\n question_list = [[] for _ in range(len(str(question)))]\n answer_list = [[] for _ in range(len(str(answer)))]\n \n # Process each image and its position\n for index, image in enumerate(images):\n ref_image.append(image[\"image\"])\n position = image[\"position\"]\n position = list(position.items())[0]\n \n # Record image indices at their insertion positions\n if position[0] == 'question':\n insert_loc = position[1] - 1\n if insert_loc < 0:\n insert_loc = 0\n question_list[insert_loc].append(index)\n if position[0] == 'answer':\n insert_loc = position[1] - 1\n if insert_loc < 0:\n insert_loc = 0\n answer_list[insert_loc].append(index)\n \n # Build new question text with image tokens\n new_question = ''\n new_answer = ''\n for char_i in range(len(question)):\n if question_list[char_i] == []:\n new_question = new_question + question[char_i]\n if question_list[char_i] != []:\n for img_index in question_list[char_i]:\n try:\n new_question = new_question + '' + self.image_padding_tokens[img_index] + ''\n except:\n print(\"Error: out of max image input size\")\n new_question = new_question + question[char_i]\n \n # Build new answer text with image tokens\n for char_i in range(len(answer)):\n if answer_list[char_i] == []:\n new_answer = new_answer + answer[char_i]\n if answer_list[char_i] != []:\n for img_index in answer_list[char_i]:\n try:\n new_answer = new_answer + '' + self.image_padding_tokens[img_index] + ''\n except:\n print(\"Error: out of max image input size\")\n new_answer = new_answer + answer[char_i]\n \n # Clean up text formatting\n new_answer = new_answer.replace('•', '')\n return ref_image, new_question, new_answer\n \n \n \n \n \n \n# torch.set_printoptions(profile=\"full\") \n# text_tokenizer = '/home/cs/leijiayu/wuchaoyi/Finetune_LLAMA/LLAMA_Model/tokenizer'\n# dataset = multi_dataset(text_tokenizer = text_tokenizer)\n# print(len(dataset))\n# for i in range(10):\n# dataset[i]\n# input()"
},
{
"path": "src/Dataset/multi_dataset_test_for_close.py",
"content": "from torch.utils.data import Dataset\nimport numpy as np\nimport transformers\nimport pandas as pd\nimport copy \nimport random \nimport os\nimport numpy as np\nimport tqdm\nimport torch\nimport json\nfrom PIL import Image\nimport math\nimport torchvision\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer\nfrom .dataset import *\n\n\ndef find_position(label, key_embeddings):\n loss_reweight = torch.ones(label.shape)\n for i in range(len(label)):\n if label[i] == -100:\n loss_reweight[i] = 0\n else:\n for key_embedding in key_embeddings:\n if torch.equal(label[i:i+len(key_embedding)], key_embedding):\n loss_reweight[i:i+len(key_embedding)] = 3\n return loss_reweight\n\ndef stack_images(images):\n \n target_H = 512\n target_W = 512\n target_D = 4\n if len(images) == 0:\n return torch.zeros((1,3,target_H,target_W,target_D))\n MAX_D = 4\n D_list = list(range(4,65,4))\n \n for ii in images:\n try:\n D = ii.shape[3]\n if D > MAX_D:\n MAX_D = D\n except:\n continue\n for temp_D in D_list:\n if abs(temp_D - MAX_D)< abs(target_D - MAX_D):\n target_D = temp_D\n \n stack_images = []\n for s in images:\n if len(s.shape) == 3:\n #print(s.shape)\n stack_images.append(torch.nn.functional.interpolate(s.unsqueeze(0).unsqueeze(-1), size = (target_H,target_W,target_D)))\n else:\n stack_images.append(torch.nn.functional.interpolate(s.unsqueeze(0), size = (target_H,target_W,target_D)))\n images = torch.cat(stack_images, dim=0)\n return images\n\nclass multi_dataset_close(Dataset):\n def __init__(self, text_tokenizer, test_split = 'close', max_seq = 2048, max_img_size = 10, image_num=32,voc_size =32000, down_sample_ratio = 100):\n \n self.down_sample_ratio = down_sample_ratio\n self.text_tokenizer = text_tokenizer\n self.max_img_size = max_img_size\n self.image_num = image_num\n self.max_seq = max_seq\n self.voc_size = voc_size\n self.H = 512\n self.W = 512\n self.image_padding_tokens = []\n if isinstance(self.text_tokenizer,str):\n self.text_tokenizer = LlamaTokenizer.from_pretrained(\n self.text_tokenizer,\n )\n special_token = {\"additional_special_tokens\": [\"\",\"\"]}\n for i in range(max_img_size):\n image_padding_token = \"\"\n for j in range(image_num):\n image_token = \"\"\n image_padding_token = image_padding_token + image_token\n special_token[\"additional_special_tokens\"].append(\"\")\n self.image_padding_tokens.append(image_padding_token)\n self.text_tokenizer.add_special_tokens(\n special_token\n )\n self.text_tokenizer.pad_token_id = 0\n self.text_tokenizer.bos_token_id = 1\n self.text_tokenizer.eos_token_id = 2\n\n\n self.data_whole_2D = []\n self.data_whole_3D = []\n self.dataset_reflect = {}\n self.test_split = test_split\n ### closed ###\n if self.test_split == 'close':\n\n chestxray_dataset_bn = Binary_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/chestxray_balance_test.csv', \n prompt_json_file = '/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json')\n self.dataset_reflect['chestxray_dataset_bn'] = chestxray_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'chestxray_dataset_bn':i} for i in range(len(chestxray_dataset_bn))]\n print('chestxray_dataset_bn loaded')\n \n pcxr_dataset_bn = Binary_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/pcxr_balance_test.csv', \n prompt_json_file = '/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json')\n self.dataset_reflect['pcxr_dataset_bn'] = pcxr_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'pcxr_dataset_bn':i} for i in range(len(pcxr_dataset_bn))]\n print('pcxr_dataset_bn loaded')\n \n mammo_dataset_bn = Binary_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/mammo_balance_test.csv', \n prompt_json_file = '/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json')\n self.dataset_reflect['mammo_dataset_bn'] = mammo_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'mammo_dataset_bn':i} for i in range(len(mammo_dataset_bn))]\n print('mammo_dataset_bn loaded')\n \n spinexr_dataset_bn = Binary_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/spinexr_balance_test.csv', \n prompt_json_file = '/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/yes_no_prompt.json')\n self.dataset_reflect['spinexr_dataset_bn'] = spinexr_dataset_bn\n self.data_whole_2D = self.data_whole_2D + [{'spinexr_dataset_bn':i} for i in range(len(spinexr_dataset_bn))]\n print('spinexr_dataset_bn loaded')\n \n radiomodality_dataset = Radio_Modality_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/radio_modality_test.csv', \n prompt_json_file = '/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/modality_prompt.json',\n modality_json_file = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/modality_set.json')\n self.dataset_reflect['radiomodality_dataset'] = radiomodality_dataset\n self.data_whole_3D = self.data_whole_3D + [{'radiomodality_dataset':i} for i in range(len(radiomodality_dataset))]\n print('radiomodality_dataset loaded')\n \n ### multi-label ###\n chestxray_dataset = ChestXray_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/chestxray_test.csv', \n prompt_json_file = '/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/cls_prompt.json')\n self.dataset_reflect['chestxray_dataset'] = chestxray_dataset\n self.data_whole_2D = self.data_whole_2D + [{'chestxray_dataset':i} for i in range(len(chestxray_dataset))]\n print('chestxray_dataset loaded')\n \n pcxr_dataset = ChestXray_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/pcxr_test.csv', \n prompt_json_file = '/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/cls_prompt.json')\n self.dataset_reflect['pcxr_dataset'] = pcxr_dataset\n self.data_whole_2D = self.data_whole_2D + [{'pcxr_dataset':i} for i in range(len(pcxr_dataset))]\n print('pcxr_dataset loaded')\n \n mammo_dataset = ChestXray_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/mammo_test.csv', \n prompt_json_file = '/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/mammo_prompt.json')\n self.dataset_reflect['mammo_dataset'] = mammo_dataset\n self.data_whole_2D = self.data_whole_2D + [{'mammo_dataset':i} for i in range(len(mammo_dataset))]\n print('mammo_dataset loaded')\n \n spinexr_dataset = ChestXray_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/spinexr_test.csv', \n prompt_json_file = '/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/dataset/spinexr_prompt.json')\n self.dataset_reflect['spinexr_dataset'] = spinexr_dataset\n self.data_whole_2D = self.data_whole_2D + [{'spinexr_dataset':i} for i in range(len(spinexr_dataset))]\n print('spinexr_dataset loaded')\n\n medpix_single_dataset = MedPix_Single_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/MedPix_single_test_close.csv')\n self.dataset_reflect['medpix_single_dataset'] = medpix_single_dataset\n self.data_whole_2D = self.data_whole_2D + [{'medpix_single_dataset':i} for i in range(len(medpix_single_dataset))]\n print('medpix_single_dataset loaded')\n \n vqarad_dataset = VQA_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/vqarad_test.csv')\n self.dataset_reflect['vqarad_dataset'] = vqarad_dataset\n self.data_whole_2D = self.data_whole_2D + [{'vqarad_dataset':i} for i in range(len(vqarad_dataset))]\n print('vqarad_dataset loaded')\n \n slake_dataset = VQA_Dataset(csv_path = '/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/New_Dataset/data_csv/slakevqa_test.csv')\n self.dataset_reflect['slake_dataset'] = slake_dataset\n self.data_whole_2D = self.data_whole_2D + [{'slake_dataset':i} for i in range(len(slake_dataset))]\n print('slake_dataset loaded')\n \n \n self.data_whole = self.data_whole_2D + self.data_whole_3D\n random.shuffle(self.data_whole)\n \n \n def __len__(self):\n return math.ceil(len(self.data_whole)/self.down_sample_ratio)\n \n def __getitem__(self, idx):\n idx = (self.down_sample_ratio*idx +random.randint(0,self.down_sample_ratio-1))%len(self.data_whole)\n # vision_x, lang_x, attention_mask, labels\n sample = list(self.data_whole[idx].items())[0]\n dataset_index = sample[0]\n #print(sample)\n sample = self.dataset_reflect[sample[0]][sample[1]] \n '''\n Dict: {\n \"image_dict\": [\n {\"image\": image, # image is a tensor of shape [c,w,h,d], c is channel=3, w is width, h is height, d is depth(1 for chestxray,pmcoa,pmcvqa)\n \"position\": {\"question\": 0}}, position is a dict, random choice of 0 or len(question)\n ]\n \"question\": question, \n \"answer\":answer, \n }\n '''\n images = sample[\"image_dict\"]\n question = str(sample[\"question\"])\n answer = str(sample[\"answer\"])\n images, question, answer = self.text_add_image(images,question,answer)\n \n # print(question,answer)\n ### make vision_x\n try:\n vision_x = stack_images(images)\n except:\n print(self.data_whole[idx].items())\n #print(vision_x.shape,question,answer)\n \n ### make lang_x ###\n self.text_tokenizer.padding_side = \"right\"\n text_tensor = self.text_tokenizer(\n question + ' ' + answer, max_length=self.max_seq, truncation=True, padding=\"max_length\", return_tensors=\"pt\"\n )\n lang_x = text_tensor[\"input_ids\"][0]\n attention_mask = text_tensor[\"attention_mask\"][0]\n try:\n lang_x[torch.sum(attention_mask)] = self.text_tokenizer.eos_token_id\n except:\n pass\n ### make label ###\n \n emphasize_words = []\n # emphasize_words = [str(_) for _ in self.words_extract.extract(answer)]\n \n if emphasize_words != []:\n emphasize_words_tensor = self.text_tokenizer(\n emphasize_words , max_length=self.max_seq\n )\n key_embeddings = [torch.tensor(_[1:]) for _ in emphasize_words_tensor['input_ids']]\n else:\n key_embeddings = []\n question_tensor = self.text_tokenizer(\n question, max_length=self.max_seq, truncation=True, padding=\"max_length\", return_tensors=\"pt\"\n )\n question_length = torch.sum(question_tensor[\"attention_mask\"][0])\n labels = lang_x.clone()\n labels[labels == self.text_tokenizer.pad_token_id] = -100\n labels[labels >= self.voc_size] = -100\n labels[:question_length] = -100\n \n reweight_tensor = find_position(labels, key_embeddings)\n if dataset_index == 'paper_inline_dataset':\n emphasize_words = []\n # print(labels,key_embeddings,reweight_tensor)\n return {'vision_x': vision_x,'lang_x':lang_x, 'attention_mask': attention_mask, 'labels':labels, 'loss_reweight': reweight_tensor, 'key_words_query': emphasize_words}\n \n def text_add_image(self,images,question,answer):\n question = str(question)\n answer = str(answer)\n ref_image = []\n question_list = [[] for _ in range(len(str(question)))]\n answer_list = [[] for _ in range(len(str(answer)))]\n for index, image in enumerate(images):\n ref_image.append(image[\"image\"])\n position = image[\"position\"]\n position = list(position.items())[0]\n if position[0] == 'question':\n insert_loc = position[1] -1\n if insert_loc < 0:\n insert_loc = 0\n question_list[insert_loc].append(index)\n if position[0] == 'answer':\n insert_loc = position[1] -1\n if insert_loc < 0:\n insert_loc = 0\n answer_list[insert_loc].append(index)\n new_question = ''\n new_answer = ''\n for char_i in range(len(question)):\n if question_list[char_i] == []:\n new_question = new_question + question[char_i]\n if question_list[char_i] != []:\n for img_index in question_list[char_i]:\n try:\n new_question = new_question + '' + self.image_padding_tokens[img_index] + ''\n except:\n print(\"Error: out of max image input size\")\n new_question = new_question + question[char_i]\n \n for char_i in range(len(answer)):\n if answer_list[char_i] == []:\n new_answer = new_answer + answer[char_i]\n if answer_list[char_i] != []:\n for img_index in answer_list[char_i]:\n try:\n new_answer = new_answer + '' + self.image_padding_tokens[img_index] + ''\n except:\n print(\"Error: out of max image input size\")\n new_answer = new_answer + answer[char_i]\n \n new_answer = new_answer.replace('•','')\n return ref_image,new_question,new_answer\n \n \n \n \n \n \n# torch.set_printoptions(profile=\"full\") \n# text_tokenizer = '/home/cs/leijiayu/wuchaoyi/Finetune_LLAMA/LLAMA_Model/tokenizer'\n# dataset = multi_dataset_close(text_tokenizer = text_tokenizer)\n# print(len(dataset))\n# for i in range(10):\n# dataset[i]\n# input()\n"
},
{
"path": "src/Model/RadFM/__init__.py",
"content": ""
},
{
"path": "src/Model/RadFM/blocks.py",
"content": "from collections import OrderedDict\nfrom typing import Tuple, Union, Callable, Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torch.utils.checkpoint import checkpoint\n\nclass PMC_CLIP_cfg:\n backbone: str = 'ModifiedRN50' # ['RN50', 'ModifiedRN50', 'MAE']\n layers: Union[Tuple[int, int, int, int], int] = [3,4,6,3]\n width: int = 64\n head_width: int = 64\n mlp_ratio: float = 4.0\n patch_size: int = 16\n image_size: Union[Tuple[int, int], int] = 224\n timm_model_name: str = None # a valid model name overrides layers, width, patch_size\n timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model\n timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')\n timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '')\n patch_dropout: float = 0.0 # patch dropout rate, no dropout by default\n drop_attention_rate: float = 0. # Transformer Dropout\n patch_size: None\n \nclass Bottleneck(nn.Module):\n expansion = 4\n\n def __init__(self, inplanes, planes, stride=1):\n super().__init__()\n\n # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1\n self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.relu1 = nn.ReLU(inplace=True)\n\n self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(planes)\n self.relu2 = nn.ReLU(inplace=True)\n\n self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()\n\n self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)\n self.bn3 = nn.BatchNorm2d(planes * self.expansion)\n self.relu3 = nn.ReLU(inplace=True)\n\n self.downsample = None\n self.stride = stride\n\n if stride > 1 or inplanes != planes * Bottleneck.expansion:\n # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1\n self.downsample = nn.Sequential(OrderedDict([\n (\"-1\", nn.AvgPool2d(stride)),\n (\"0\", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),\n (\"1\", nn.BatchNorm2d(planes * self.expansion))\n ]))\n\n def forward(self, x: torch.Tensor):\n identity = x\n\n out = self.relu1(self.bn1(self.conv1(x)))\n out = self.relu2(self.bn2(self.conv2(out)))\n out = self.avgpool(out)\n out = self.bn3(self.conv3(out))\n\n if self.downsample is not None:\n identity = self.downsample(x)\n\n out += identity\n out = self.relu3(out)\n return out\n\n\nclass AttentionPool2d(nn.Module):\n def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):\n super().__init__()\n self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)\n self.k_proj = nn.Linear(embed_dim, embed_dim)\n self.q_proj = nn.Linear(embed_dim, embed_dim)\n self.v_proj = nn.Linear(embed_dim, embed_dim)\n self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)\n self.num_heads = num_heads\n\n def forward(self, x):\n x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC\n x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC\n x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC\n x, _ = F.multi_head_attention_forward(\n query=x, key=x, value=x,\n embed_dim_to_check=x.shape[-1],\n num_heads=self.num_heads,\n q_proj_weight=self.q_proj.weight,\n k_proj_weight=self.k_proj.weight,\n v_proj_weight=self.v_proj.weight,\n in_proj_weight=None,\n in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),\n bias_k=None,\n bias_v=None,\n add_zero_attn=False,\n dropout_p=0,\n out_proj_weight=self.c_proj.weight,\n out_proj_bias=self.c_proj.bias,\n use_separate_proj_weight=True,\n training=self.training,\n need_weights=False\n )\n\n return x[0]\n\n\nclass ResNet(nn.Module):\n \"\"\"\n RN50\n \"\"\"\n\n def __init__(\n self, layers, output_dim, heads, image_size=224, width=64,\n block=Bottleneck,\n ):\n super().__init__()\n self.output_dim = output_dim\n self.image_size = image_size\n\n # the 1-layer stem\n self.conv1 = nn.Conv2d(3, width, kernel_size=3, stride=2, padding=1, bias=False)\n self.bn1 = nn.BatchNorm2d(width)\n self.relu1 = nn.ReLU(inplace=True)\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n # residual layers\n self._inplanes = width # this is a *mutable* variable used during construction\n self.layer1 = self._make_layer(width, layers[0])\n self.layer2 = self._make_layer(width * 2, layers[1], stride=2)\n self.layer3 = self._make_layer(width * 4, layers[2], stride=2)\n self.layer4 = self._make_layer(width * 8, layers[3], stride=2)\n self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n # self.head = nn.Linear(512 * 6, output_dim)\n self.head = nn.Linear(512 * block.expansion, output_dim)\n\n # embed_dim = width * 32 # the ResNet feature dimension\n # self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)\n\n self.init_parameters()\n\n def _make_layer(\n self,\n planes, blocks, stride=1,\n block=Bottleneck,\n ):\n layers = [block(self._inplanes, planes, stride)]\n\n self._inplanes = planes * block.expansion\n for _ in range(1, blocks):\n layers.append(block(self._inplanes, planes))\n\n return nn.Sequential(*layers)\n\n def init_parameters(self):\n for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:\n for name, param in resnet_block.named_parameters():\n if name.endswith(\"bn3.weight\"):\n nn.init.zeros_(param)\n\n def lock(self, unlocked_groups=0, freeze_bn_stats=False):\n assert unlocked_groups == 0, 'partial locking not currently supported for this model'\n for param in self.parameters():\n param.requires_grad = False\n if freeze_bn_stats:\n freeze_batch_norm_2d(self)\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n # FIXME support for non-transformer\n pass\n\n def stem(self, x):\n x = self.relu1(self.bn1(self.conv1(x)))\n x = self.maxpool(x)\n return x\n\n def forward(self, x):\n # x[0]: [batch_size, 3, 224, 224]\n # x[1]: [batch_size, 1]\n x = self.stem(x) # [batch_size, 64, 56, 56]\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x) # [batch_size, 2048, 7, 7]\n x = self.avgpool(x) # [batch_size, 2048, 1, 1]\n x = torch.flatten(x, 1) # [batch_size, 2048*1*1]\n x = self.head(x) # [batch_size, 1024]\n\n visual_output = dict.fromkeys([\"image_features\", \"mim_loss\"], None)\n visual_output.update({\n 'image_features': x,\n })\n\n return visual_output\n\n\nclass ModifiedResNet(nn.Module):\n \"\"\"\n A ResNet class that is similar to torchvision's but contains the following changes:\n - There are now 3 \"stem\" convolutions as opposed to 1, with an average pool instead of a max pool.\n - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1\n - The final pooling layer is a QKV attention instead of an average pool\n \"\"\"\n\n def __init__(self, layers, output_dim, heads, image_size=224, width=64):\n super().__init__()\n self.output_dim = output_dim\n self.image_size = image_size\n\n # the 3-layer stem\n self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)\n self.bn1 = nn.BatchNorm2d(width // 2)\n self.relu1 = nn.ReLU(inplace=True)\n self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(width // 2)\n self.relu2 = nn.ReLU(inplace=True)\n self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)\n self.bn3 = nn.BatchNorm2d(width)\n self.relu3 = nn.ReLU(inplace=True)\n self.avgpool = nn.AvgPool2d(2)\n\n # residual layers\n self._inplanes = width # this is a *mutable* variable used during construction\n self.layer1 = self._make_layer(width, layers[0])\n self.layer2 = self._make_layer(width * 2, layers[1], stride=2)\n self.layer3 = self._make_layer(width * 4, layers[2], stride=2)\n self.layer4 = self._make_layer(width * 8, layers[3], stride=2)\n\n embed_dim = width * 32 # the ResNet feature dimension\n self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)\n\n self.init_parameters()\n\n def _make_layer(self, planes, blocks, stride=1):\n layers = [Bottleneck(self._inplanes, planes, stride)]\n\n self._inplanes = planes * Bottleneck.expansion\n for _ in range(1, blocks):\n layers.append(Bottleneck(self._inplanes, planes))\n\n return nn.Sequential(*layers)\n\n def init_parameters(self):\n if self.attnpool is not None:\n std = self.attnpool.c_proj.in_features ** -0.5\n nn.init.normal_(self.attnpool.q_proj.weight, std=std)\n nn.init.normal_(self.attnpool.k_proj.weight, std=std)\n nn.init.normal_(self.attnpool.v_proj.weight, std=std)\n nn.init.normal_(self.attnpool.c_proj.weight, std=std)\n\n for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:\n for name, param in resnet_block.named_parameters():\n if name.endswith(\"bn3.weight\"):\n nn.init.zeros_(param)\n\n def lock(self, unlocked_groups=0, freeze_bn_stats=False):\n assert unlocked_groups == 0, 'partial locking not currently supported for this model'\n for param in self.parameters():\n param.requires_grad = False\n if freeze_bn_stats:\n freeze_batch_norm_2d(self)\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n # FIXME support for non-transformer\n pass\n\n def stem(self, x):\n x = self.relu1(self.bn1(self.conv1(x)))\n x = self.relu2(self.bn2(self.conv2(x)))\n x = self.relu3(self.bn3(self.conv3(x)))\n x = self.avgpool(x)\n return x\n\n def forward(self, x):\n x = self.stem(x)\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n x = self.attnpool(x)\n\n visual_output = dict.fromkeys([\"image_features\", \"mim_loss\"], None)\n visual_output.update({\n 'image_features': x,\n })\n\n return visual_output\n\n\nclass LayerNorm(nn.LayerNorm):\n \"\"\"Subclass torch's LayerNorm to handle fp16.\"\"\"\n\n def forward(self, x: torch.Tensor):\n orig_type = x.dtype\n x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)\n return x.to(orig_type)\n\n\nclass QuickGELU(nn.Module):\n # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\n\nclass ResidualAttentionBlock(nn.Module):\n def __init__(\n self, d_model: int, n_head: int, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU,\n drop_attention_rate: float = 0.,\n ):\n super().__init__()\n\n self.attn = nn.MultiheadAttention(\n embed_dim=d_model,\n num_heads=n_head,\n dropout=drop_attention_rate,\n )\n self.ln_1 = LayerNorm(d_model)\n mlp_width = int(d_model * mlp_ratio)\n self.mlp = nn.Sequential(OrderedDict([\n (\"c_fc\", nn.Linear(d_model, mlp_width)),\n (\"gelu\", act_layer()),\n (\"c_proj\", nn.Linear(mlp_width, d_model))\n ]))\n self.ln_2 = LayerNorm(d_model)\n\n def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):\n return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0]\n\n def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):\n x = x + self.attention(self.ln_1(x), attn_mask=attn_mask)\n x = x + self.mlp(self.ln_2(x))\n return x\n\n\nclass PatchDropout(nn.Module):\n \"\"\"\n https://arxiv.org/abs/2212.00794\n \"\"\"\n\n def __init__(self, prob, exclude_first_token=True):\n super().__init__()\n assert 0 <= prob < 1.\n self.prob = prob\n self.exclude_first_token = exclude_first_token # exclude CLS token\n\n def forward(self, x):\n if not self.training or self.prob == 0.:\n return x\n\n if self.exclude_first_token:\n cls_tokens, x = x[:, :1], x[:, 1:]\n else:\n cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1])\n\n batch = x.size()[0]\n num_tokens = x.size()[1]\n\n batch_indices = torch.arange(batch)\n batch_indices = batch_indices[..., None]\n\n keep_prob = 1 - self.prob\n num_patches_keep = max(1, int(num_tokens * keep_prob))\n\n rand = torch.randn(batch, num_tokens)\n patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices\n\n x = x[batch_indices, patch_indices_keep]\n\n if self.exclude_first_token:\n x = torch.cat((cls_tokens, x), dim=1)\n\n return x\n\n\nclass Transformer(nn.Module):\n def __init__(\n self, width: int, layers: int, heads: int, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU,\n drop_attention_rate: float = 0.,\n ):\n super().__init__()\n self.width = width\n self.layers = layers\n self.grad_checkpointing = False\n\n self.resblocks = nn.ModuleList([\n ResidualAttentionBlock(width, heads, mlp_ratio, act_layer=act_layer, drop_attention_rate=drop_attention_rate)\n for _ in range(layers)\n ])\n\n def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):\n for r in self.resblocks:\n if self.grad_checkpointing and not torch.jit.is_scripting():\n x = checkpoint(r, x, attn_mask)\n else:\n x = r(x, attn_mask=attn_mask)\n return x"
},
{
"path": "src/Model/RadFM/helpers.py",
"content": "\"\"\"\nTaken from https://github.com/lucidrains/flamingo-pytorch\n\"\"\"\n\nimport torch\nfrom einops import rearrange, repeat\nfrom einops_exts import rearrange_many\nfrom torch import einsum, nn\n\n\ndef exists(val):\n return val is not None\n\n\ndef FeedForward(dim, mult=4):\n inner_dim = int(dim * mult)\n return nn.Sequential(\n nn.LayerNorm(dim),\n nn.Linear(dim, inner_dim, bias=False),\n nn.GELU(),\n nn.Linear(inner_dim, dim, bias=False),\n )\n\n\nclass PerceiverAttention(nn.Module):\n def __init__(self, *, dim, dim_head=64, heads=8):\n super().__init__()\n self.scale = dim_head**-0.5\n self.heads = heads\n inner_dim = dim_head * heads\n\n self.norm_media = nn.LayerNorm(dim)\n self.norm_latents = nn.LayerNorm(dim)\n\n self.to_q = nn.Linear(dim, inner_dim, bias=False)\n self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)\n self.to_out = nn.Linear(inner_dim, dim, bias=False)\n\n def forward(self, x, latents):\n \"\"\"\n Args:\n x (torch.Tensor): image features\n shape (b, T, n1, D)\n latent (torch.Tensor): latent features\n shape (b, T, n2, D)\n \"\"\"\n x = self.norm_media(x)\n latents = self.norm_latents(latents)\n\n h = self.heads\n\n q = self.to_q(latents)\n kv_input = torch.cat((x, latents), dim=-2)\n k, v = self.to_kv(kv_input).chunk(2, dim=-1)\n q, k, v = rearrange_many((q, k, v), \"b t n (h d) -> b h t n d\", h=h)\n q = q * self.scale\n\n # attention\n sim = einsum(\"... i d, ... j d -> ... i j\", q, k)\n sim = sim - sim.amax(dim=-1, keepdim=True).detach()\n attn = sim.softmax(dim=-1)\n\n out = einsum(\"... i j, ... j d -> ... i d\", attn, v)\n out = rearrange(out, \"b h t n d -> b t n (h d)\", h=h)\n return self.to_out(out)\n\n\nclass PerceiverResampler(nn.Module):\n def __init__(\n self,\n *,\n dim,\n depth=6,\n dim_head=64,\n heads=8,\n num_latents=64,\n max_num_media=None,\n max_num_frames=None,\n ff_mult=4,\n ):\n super().__init__()\n self.latents = nn.Parameter(torch.randn(num_latents, dim))\n self.frame_embs = (\n nn.Parameter(torch.randn(max_num_frames, dim))\n if exists(max_num_frames)\n else None\n )\n self.media_time_embs = (\n nn.Parameter(torch.randn(max_num_media, 1, dim))\n if exists(max_num_media)\n else None\n )\n\n self.layers = nn.ModuleList([])\n for _ in range(depth):\n self.layers.append(\n nn.ModuleList(\n [\n PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),\n FeedForward(dim=dim, mult=ff_mult),\n ]\n )\n )\n\n self.norm = nn.LayerNorm(dim)\n\n def forward(self, x):\n \"\"\"\n Args:\n x (torch.Tensor): image features\n shape (b, T, F, v, D)\n Returns:\n shape (b, T, n, D) where n is self.num_latents\n \"\"\"\n b, T, F, v = x.shape[:4]\n\n # frame and media time embeddings\n if exists(self.frame_embs):\n frame_embs = repeat(self.frame_embs[:F], \"F d -> b T F v d\", b=b, T=T, v=v)\n x = x + frame_embs\n x = rearrange(\n x, \"b T F v d -> b T (F v) d\"\n ) # flatten the frame and spatial dimensions\n if exists(self.media_time_embs):\n x = x + self.media_time_embs[:T]\n\n # blocks\n latents = repeat(self.latents, \"n d -> b T n d\", b=b, T=T)\n for attn, ff in self.layers:\n latents = attn(x, latents) + latents\n latents = ff(latents) + latents\n return self.norm(latents)\n\n\n# gated cross attention\n\n\nclass MaskedCrossAttention(nn.Module):\n def __init__(\n self,\n *,\n dim,\n dim_visual,\n dim_head=64,\n heads=8,\n only_attend_immediate_media=True,\n ):\n super().__init__()\n self.scale = dim_head**-0.5\n self.heads = heads\n inner_dim = dim_head * heads\n\n self.norm = nn.LayerNorm(dim)\n\n self.to_q = nn.Linear(dim, inner_dim, bias=False)\n self.to_kv = nn.Linear(dim_visual, inner_dim * 2, bias=False)\n self.to_out = nn.Linear(inner_dim, dim, bias=False)\n\n # whether for text to only attend to immediate preceding image, or all previous images\n self.only_attend_immediate_media = only_attend_immediate_media\n\n def forward(self, x, media, media_locations=None, attend_previous=True):\n \"\"\"\n Args:\n x (torch.Tensor): text features\n shape (B, T_txt, D_txt)\n media (torch.Tensor): image features\n shape (B, T_img, n, D_img) where n is the dim of the latents\n media_locations: boolean mask identifying the media tokens in x\n shape (B, T_txt)\n attend_previous: bool\n If false, ignores immediately preceding image and starts attending when following image\n \"\"\"\n _, T_img, n = media.shape[:3]\n h = self.heads\n\n x = self.norm(x)\n\n q = self.to_q(x)\n media = rearrange(media, \"b t n d -> b (t n) d\")\n\n k, v = self.to_kv(media).chunk(2, dim=-1)\n q, k, v = rearrange_many((q, k, v), \"b n (h d) -> b h n d\", h=h)\n\n q = q * self.scale\n\n sim = einsum(\"... i d, ... j d -> ... i j\", q, k)\n\n if exists(media_locations):\n # at each boolean of True, increment the time counter (relative to media time)\n text_time = media_locations.cumsum(dim=-1)\n media_time = torch.arange(T_img, device=x.device) + 1\n\n if not attend_previous:\n text_time[~media_locations] += 1\n # make sure max is still the number of images in the sequence\n text_time[\n text_time\n > repeat(\n torch.count_nonzero(media_locations, dim=1),\n \"b -> b i\",\n i=text_time.shape[1],\n )\n ] = 0\n\n # text time must equal media time if only attending to most immediate image\n # otherwise, as long as text time is greater than media time (if attending to all previous images / media)\n mask_op = torch.eq if self.only_attend_immediate_media else torch.ge\n\n text_to_media_mask = mask_op(\n rearrange(text_time, \"b i -> b 1 i 1\"),\n repeat(media_time, \"j -> 1 1 1 (j n)\", n=n),\n )\n sim = sim.masked_fill(~text_to_media_mask, -torch.finfo(sim.dtype).max)\n\n sim = sim - sim.amax(dim=-1, keepdim=True).detach()\n attn = sim.softmax(dim=-1)\n\n if exists(media_locations) and self.only_attend_immediate_media:\n # any text without a preceding media needs to have attention zeroed out\n text_without_media_mask = text_time == 0\n text_without_media_mask = rearrange(\n text_without_media_mask, \"b i -> b 1 i 1\"\n )\n attn = attn.masked_fill(text_without_media_mask, 0.0)\n\n out = einsum(\"... i j, ... j d -> ... i d\", attn, v)\n out = rearrange(out, \"b h n d -> b n (h d)\")\n return self.to_out(out)\n\n\nclass GatedCrossAttentionBlock(nn.Module):\n def __init__(\n self,\n *,\n dim,\n dim_visual,\n dim_head=64,\n heads=8,\n ff_mult=4,\n only_attend_immediate_media=True,\n ):\n super().__init__()\n self.attn = MaskedCrossAttention(\n dim=dim,\n dim_visual=dim_visual,\n dim_head=dim_head,\n heads=heads,\n only_attend_immediate_media=only_attend_immediate_media,\n )\n self.attn_gate = nn.Parameter(torch.tensor([0.0]))\n\n self.ff = FeedForward(dim, mult=ff_mult)\n self.ff_gate = nn.Parameter(torch.tensor([0.0]))\n\n def forward(\n self,\n x,\n media,\n media_locations=None,\n attend_previous=True,\n ):\n x = (\n self.attn(\n x,\n media,\n media_locations=media_locations,\n attend_previous=attend_previous,\n )\n * self.attn_gate.tanh()\n + x\n )\n x = self.ff(x) * self.ff_gate.tanh() + x\n\n return x\n"
},
{
"path": "src/Model/RadFM/multimodality_model.py",
"content": "# Import necessary libraries\nfrom torch import nn\nfrom transformers.models.llama import LlamaForCausalLM\nfrom .my_embedding_layer import MyEmbedding\nfrom torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\nimport tqdm.auto as tqdm\nimport torch.nn as nn\nimport torch\nfrom torch.utils.checkpoint import checkpoint\nfrom torch.autograd import Variable\nimport numpy as np\n\nclass MultiLLaMAForCausalLM(nn.Module):\n \"\"\"\n A multimodal LLaMA model that combines language and vision inputs\n for causal language modeling tasks.\n \"\"\"\n def __init__(self, lang_model_path): \n \"\"\"\n Initialize the multimodal model.\n \n Args:\n lang_model_path (str): Path to the pretrained language model\n \"\"\"\n super(MultiLLaMAForCausalLM, self).__init__() \n \n # Load pretrained LLaMA model\n self.lang_model = LlamaForCausalLM.from_pretrained(\n lang_model_path,\n )\n \n # Enable gradient checkpointing for memory efficiency\n self.lang_model.gradient_checkpointing_enable()\n self.lang_model.enable_input_require_grads()\n \n # Initialize custom embedding layer and share weights with language model\n self.embedding_layer = MyEmbedding()\n self.embedding_layer.weight = self.lang_model.get_input_embeddings().weight\n \n # Set model dimensions\n self.hidden_dim = 5120\n self.voc_size = 32000\n \n def forward(self, lang_x, vision_x, attention_mask, labels, loss_reweight, key_words_query):\n \"\"\"\n Forward pass for the multimodal model.\n \n Args:\n lang_x: Language input tokens\n vision_x: Vision input features\n attention_mask: Attention mask for language inputs\n labels: Target labels for language modeling\n loss_reweight: Weights for calculating loss (to prioritize certain tokens)\n key_words_query: Query for highlighting important words\n \n Returns:\n Dictionary containing model outputs including loss and logits\n \"\"\"\n if labels.shape == lang_x.shape:\n # Set embedding mode to handle text inputs\n self.embedding_layer.flag = 'Text'\n \n # Get embeddings and matching loss from embedding layer\n input_embedding, loss_match = self.embedding_layer(lang_x, vision_x, key_words_query)\n \n # Forward pass through the language model\n output = self.lang_model(inputs_embeds=input_embedding, attention_mask=attention_mask, labels=labels)\n logits = output['logits']\n\n # Initialize regularization loss\n loss_reg = None\n if labels is not None:\n # Shift logits and labels for next-token prediction\n shift_logits = logits[..., :-1, :].contiguous()\n shift_labels = labels[..., 1:].contiguous()\n shift_loss_reweight = loss_reweight[..., 1:].contiguous()\n \n # Prepare for loss calculation\n loss_fct = CrossEntropyLoss(reduction='none')\n shift_logits = shift_logits.view(-1, self.voc_size)\n shift_labels = shift_labels.view(-1)\n shift_loss_reweight = shift_loss_reweight.view(-1)\n \n # Ensure tensors are on the same device\n shift_labels = shift_labels.to(shift_logits.device)\n shift_loss_reweight = shift_loss_reweight.to(shift_logits.device) \n \n # Calculate weighted cross-entropy loss\n loss_reg = loss_fct(shift_logits, shift_labels)\n loss_reg = torch.sum(shift_loss_reweight * loss_reg) / torch.sum(shift_loss_reweight)\n \n # Combine losses\n loss = loss_reg\n if loss_match is not None:\n loss = 0.8 * loss + 0.2 * loss_match\n \n # Calculate accuracy metrics\n logits = output['logits'][..., :-1, :].contiguous().detach()\n total = len(labels)\n predictions = torch.argmax(logits, dim=-1)\n labels = labels[..., 1:].contiguous()\n \n # Count correct predictions (ignoring padding tokens with -100)\n Acc = torch.sum(torch.all(torch.logical_or(predictions == labels, labels == -100), dim=-1)) \n Accuracy = Acc / total \n \n return dict(\n # loss_reg = loss_reg,\n # loss_matching = loss_matching,\n logits=Accuracy,\n loss=output['loss'],\n )\n \n ### useless for now ignore the folowing codes ###\n # if labels.shape == vision_x.shape:\n # self.embedding_layer.flag = 'Seg'\n # input_embedding = self.embedding_layer(lang_x, vision_x)\n \n def generate(self, lang_x, vision_x):\n \"\"\"\n Generate text based on language and vision inputs.\n \n Args:\n lang_x: Language input tokens\n vision_x: Vision input features\n \n Returns:\n Generated token sequence\n \"\"\"\n # Set embedding mode to text generation\n self.embedding_layer.flag = 'Text'\n \n with torch.no_grad():\n # Get embeddings from the embedding layer\n input_embedding, _ = self.embedding_layer(lang_x, vision_x) \n \n # Generate text using language model\n generation = self.lang_model.generate(\n inputs_embeds=input_embedding, \n max_new_tokens=200,\n top_k=50\n )\n \n return generation"
},
{
"path": "src/Model/RadFM/my_embedding_layer.py",
"content": "# Import necessary libraries\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch \nfrom .helpers import PerceiverResampler \nfrom .utils import get_visual_encoder\nfrom einops import rearrange, repeat\nfrom einops_exts import rearrange_many\nimport torchvision\nfrom .vit_3d import ViT\nfrom einops.layers.torch import Rearrange\nfrom .transformer_decoder import TransformerDecoder, TransformerDecoderLayer\nfrom torch.utils.checkpoint import checkpoint\nfrom torch.autograd import Variable\nimport random\nfrom transformers import AutoTokenizer, AutoModel\n\nclass MyEmbedding(nn.Module):\n \"\"\"\n Custom embedding layer for multimodal inputs that combines text and vision features.\n \"\"\"\n def __init__(self, num_embeddings=32000, embedding_dim=5120, perceiver_num=32, vis_dim=768, \n patch_size=32, frame_patch_size=4, seg_channel=256):\n \"\"\"\n Initialize the multimodal embedding layer.\n \n Args:\n num_embeddings (int): Size of vocabulary for text embeddings\n embedding_dim (int): Dimension of output embeddings\n perceiver_num (int): Number of latent vectors in perceiver\n vis_dim (int): Dimension of vision features\n patch_size (int): Size of image patches\n frame_patch_size (int): Size of 3D frame patches\n seg_channel (int): Number of segmentation channels\n \"\"\"\n super().__init__()\n self.num_embeddings = num_embeddings\n self.embedding_dim = embedding_dim\n # Standard embedding weight matrix for text tokens\n self.weight = nn.Parameter(torch.torch.randn((num_embeddings, embedding_dim)))\n # Special token weights for figures/images\n self.figure_token_weight = nn.Parameter(torch.randn((2, embedding_dim)))\n self.flag = 'Text' # Mode flag: 'Text' or 'Seg'\n self.patch_size = patch_size \n self.frame_patch_size = frame_patch_size\n self.seg_channel = seg_channel\n \n ## the MedKEBERT can be downloaded from https://huggingface.co/xmcmic/Med-KEBERT/tree/main ##\n # Initialize medical domain BERT model for keyword understanding\n self.bert_tokenizer = AutoTokenizer.from_pretrained(\"xmcmic/Med-KEBERT\")\n self.bert_model = AutoModel.from_pretrained(\"xmcmic/Med-KEBERT\")\n # Project BERT outputs to vision feature space\n self.bert_projection_fc = nn.Linear(768, vis_dim)\n \n # 3D Vision Transformer for processing volumetric medical images\n self.vision_encoder = ViT(\n image_size=512, # image size\n frames=512, # max number of frames\n image_patch_size=patch_size, # image patch size\n frame_patch_size=frame_patch_size, # frame patch size\n dim=vis_dim,\n depth=12,\n heads=8,\n mlp_dim=2048,\n dropout=0.1,\n emb_dropout=0.1\n )\n \n # Upscaling layers for vision features (used in segmentation mode)\n self.output_upscaling = nn.Sequential(\n nn.ConvTranspose3d(vis_dim, vis_dim // 4, kernel_size=2, stride=2),\n nn.BatchNorm3d(vis_dim // 4),\n nn.GELU(),\n nn.ConvTranspose3d(vis_dim // 4, vis_dim // 8, kernel_size=2, stride=2),\n nn.GELU(),\n )\n \n # Transformer decoder for cross-attention between text and vision\n decoder_layer = TransformerDecoderLayer(d_model=vis_dim, nhead=8, normalize_before=True)\n decoder_norm = nn.LayerNorm(vis_dim)\n self.transformer_decoder = TransformerDecoder(decoder_layer=decoder_layer, num_layers=4, norm=decoder_norm)\n \n # MLP for processing transformer decoder outputs\n self.transformer_decoder_mlp = nn.Sequential(\n nn.Linear(vis_dim, vis_dim // 4),\n nn.GELU(),\n nn.Linear(vis_dim // 4, vis_dim // 8),\n nn.GELU(),\n )\n self.vis_dim = vis_dim\n \n # Perceiver resampler to reduce sequence length of vision features\n self.perceiver = PerceiverResampler(dim=self.vis_dim, num_latents=perceiver_num)\n # Final projection to embedding dimension\n self.fc = nn.Linear(self.vis_dim, self.embedding_dim)\n # Classification head for matching keywords\n self.cls_head = nn.Linear(self.vis_dim // 8, 1)\n \n\n def forward(self, text_input, vision_x, key_words_query=None):\n \"\"\"\n Forward pass for the embedding layer.\n \n Args:\n text_input: Text token indices [B, L]\n vision_x: Visual input features [B, S, C, H, W, D]\n key_words_query: Optional list of key words for contrastive learning\n \n Returns:\n tuple: (output_embeddings, loss_matching)\n - output_embeddings: Combined embeddings for text and vision\n - loss_matching: Contrastive loss for keyword matching (or None)\n \"\"\"\n if self.flag == 'Text':\n # Process in text mode\n B, S, C, H, W, D = vision_x.shape\n # Reshape for batch processing\n vision_x = rearrange(vision_x, \"b S c h w d-> (b S) c h w d\")\n \n # Process through vision encoder\n vision_x, pos_embedding = self.vision_encoder(vision_x)\n \n # Reshape back to batch format\n vision_x = rearrange(vision_x, \"(b s F) v d -> b s F v d\", b=B, s=S, F=1) \n \n loss_matching = None\n \n if key_words_query is not None:\n ## we do not use the following parts in final version. \n ## You can quota the following codes and if so the bert models will be useless.\n # key_words_query list[list[str]] B, words, each word matches corresponding vision_x embedding\n \n # Extract and deduplicate keywords\n query_words = [item for sublist in key_words_query for item in sublist]\n query_words = list(set(query_words))\n \n # Limit number of keywords to process\n if len(query_words) > 16:\n random.shuffle(query_words)\n query_words = query_words[0:16]\n \n if query_words != []:\n # Create binary labels for contrastive learning\n contrastive_labels = torch.zeros(B, len(query_words)) # B Q\n for i, sublist in enumerate(key_words_query):\n for j, item in enumerate(query_words):\n if item in sublist:\n contrastive_labels[i, j] = 1 \n contrastive_labels = contrastive_labels.to(vision_x.dtype).to(vision_x.device) \n \n # Get BERT embeddings for keywords\n with torch.no_grad():\n query_words_embedding = self.bert_tokenizer(\n query_words, \n padding='max_length', \n truncation=True, \n max_length=256,\n return_tensors=\"pt\"\n )\n query_words_embedding = self.bert_model(\n input_ids=query_words_embedding['input_ids'].to(vision_x.device),\n attention_mask=query_words_embedding['attention_mask'].to(vision_x.device)\n )['last_hidden_state'][:, 0, :].to(vision_x.dtype).to(vision_x.device) # Q,D\n \n # Project BERT embeddings to vision space\n query_words_embedding = self.bert_projection_fc(query_words_embedding)\n query_words_embedding = query_words_embedding.unsqueeze(0).repeat(B, 1, 1) # B,Q,D\n _, N, _ = query_words_embedding.shape\n \n # Pool vision features\n image_embedding = vision_x.mean(dim=1) # B V D average pooling to remove multimodality\n image_embedding = rearrange(image_embedding, \"b F v d -> b (F v) d\")\n pos_embedding = rearrange(pos_embedding, \"(b s) v d -> b s v d\", b=B, s=S)[:, 0, :, :]\n \n # Prepare inputs for transformer decoder\n image_embedding = image_embedding.transpose(0, 1) # (H/P W/P D/P) B D\n pos_embedding = pos_embedding.transpose(0, 1) # (H/P W/P D/P) B D\n query_words_embedding = query_words_embedding.transpose(0, 1) # N B D\n \n # Cross-attention between keywords and image features\n oo_embedding, _ = self.transformer_decoder(\n query_words_embedding, image_embedding, pos=pos_embedding\n ) \n oo_embedding = oo_embedding.transpose(0, 1) # B Q D\n oo_embedding = rearrange(oo_embedding, 'b n d -> (b n) d')\n oo_embedding = self.transformer_decoder_mlp(oo_embedding)\n oo_embedding = self.cls_head(oo_embedding).mean(dim=-1)\n oo_embedding = rearrange(oo_embedding, '(b n) -> b n', b=B, n=N) # B Q \n \n # Calculate contrastive loss\n loss_matching = F.binary_cross_entropy_with_logits(oo_embedding, contrastive_labels) \n \n # Process vision features through perceiver resampler\n vision_x = self.perceiver(vision_x) # reshapes to (b, S, n, d)\n \n n = vision_x.shape[2]\n \n # Project vision features to embedding dimension\n vision_x = rearrange(vision_x, \"b s n d -> (b s n) d\")\n vision_x = self.fc(vision_x)\n vision_x = rearrange(vision_x, \"(b T) d -> b T d\", b=B, T=n*S)\n \n # Combine text and vision embeddings\n embedding_weight = torch.cat([self.weight, self.figure_token_weight], dim=0)\n embedding_weight = embedding_weight.unsqueeze(0).repeat(B, 1, 1)\n embedding_weight = torch.cat([embedding_weight, vision_x], dim=1)\n \n # Convert text indices to one-hot and compute final embeddings\n text_input = F.one_hot(text_input, embedding_weight.shape[1]).to(vision_x.dtype).to(vision_x.device)\n out_put = torch.matmul(text_input, embedding_weight)\n \n ## useless for now. ignore the folowing code## \n # if self.flag == 'Seg':\n # B,C,H,W,D = vision_x.shape\n # _,N,_ = text_input.shape\n # latent_embedding, pos_embedding = self.vision_encoder(vision_x) # B (H/P W/P D/P) D\n \n # image_embedding = latent_embedding.transpose(0,1) # (H/P W/P D/P) B D\n # pos_embedding = pos_embedding.transpose(0,1) # (H/P W/P D/P) B D\n # text_input = text_input.transpose(0,1) # N B D\n \n # mask_embedding,_ = self.transformer_decoder(text_input, image_embedding, pos = pos_embedding) \n # mask_embedding = mask_embedding.transpose(0,1) # B N D\n # mask_embedding = rearrange(mask_embedding, 'b n d -> (b n) d')\n # mask_embedding = self.transformer_decoder_mlp(mask_embedding)\n # mask_embedding = rearrange(mask_embedding, '(b n) d -> b n d', b=B, n=N,d = self.vis_dim // 8)\n \n # vision_x = rearrange(latent_embedding,'b (h w d) c -> b c h w d', h = (H // self.patch_size), w = (W // self.patch_size), d = (D // self.frame_patch_size), c=self.vis_dim)\n # vision_x = self.output_upscaling(vision_x) #B C H/4 W/4 D/4\n # out_put = torch.einsum('bchwd,bnc->bnhwd', vision_x, mask_embedding)\n \n return out_put, loss_matching\n\n# model = MyEmbedding(vision_encoder_path = '')\n# text_input = torch.randint(low=0, high=3210, size=(4,2048))\n# image_input = torch.randn((4,3,3,512,512,4))\n# key_words_query = [[],[],[],['consoliation']]\n# print(model(text_input, image_input, key_words_query))"
},
{
"path": "src/Model/RadFM/position_encoding.py",
"content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nVarious positional encodings for the transformer.\n\"\"\"\nimport math\nimport torch\nfrom torch import nn\nfrom einops.layers.torch import Rearrange\nfrom einops import rearrange, repeat\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention is all you need paper, generalized to work on images.\n \"\"\"\n def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):\n super().__init__()\n self.num_pos_feats = num_pos_feats\n self.temperature = temperature\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n def forward(self, tensor_list):\n x = tensor_list.tensors\n mask = tensor_list.mask\n assert mask is not None\n not_mask = ~mask\n y_embed = not_mask.cumsum(1, dtype=torch.float32)\n x_embed = not_mask.cumsum(2, dtype=torch.float32)\n if self.normalize:\n eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)\n pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n return pos\n\n\nclass PositionEmbeddingLearned(nn.Module):\n \"\"\"\n Absolute pos embedding, learned.\n \"\"\"\n def __init__(self, num_pos_feats=256):\n super().__init__()\n self.row_embed = nn.Embedding(50, num_pos_feats)\n self.col_embed = nn.Embedding(50, num_pos_feats)\n self.reset_parameters()\n\n def reset_parameters(self):\n nn.init.uniform_(self.row_embed.weight)\n nn.init.uniform_(self.col_embed.weight)\n\n def forward(self, tensor_list):\n x = tensor_list.tensors\n h, w = x.shape[-2:]\n i = torch.arange(w, device=x.device)\n j = torch.arange(h, device=x.device)\n x_emb = self.col_embed(i)\n y_emb = self.row_embed(j)\n pos = torch.cat([\n x_emb.unsqueeze(0).repeat(h, 1, 1),\n y_emb.unsqueeze(1).repeat(1, w, 1),\n ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1)\n return pos\n\nclass PositionEmbeddingLearned3d(nn.Module):\n \"\"\"\n Absolute pos embedding, learned.\n \"\"\"\n def __init__(self, num_pos_feats=256,h_patch_num = 16, w_patch_num = 16,d_patch_num = 64):\n super().__init__()\n self.h_patch_num = h_patch_num\n self.w_patch_num = w_patch_num\n self.d_patch_num = d_patch_num\n self.row_embed = nn.Embedding(h_patch_num, num_pos_feats)\n self.col_embed = nn.Embedding(w_patch_num, num_pos_feats)\n self.dep_embed = nn.Embedding(d_patch_num, num_pos_feats)\n self.reset_parameters()\n\n def reset_parameters(self):\n nn.init.uniform_(self.row_embed.weight)\n nn.init.uniform_(self.col_embed.weight)\n nn.init.uniform_(self.dep_embed.weight)\n\n def forward(self, B, h, w, d,x):\n i = (torch.arange(h, device=x.device) + 1)* (self.h_patch_num // h) -1\n j = (torch.arange(w, device=x.device) + 1)* (self.w_patch_num // w) -1\n k = (torch.arange(d, device=x.device) + 1)* (self.d_patch_num // d) -1\n x_emb = self.row_embed(i).unsqueeze(1).unsqueeze(2).repeat(1,w,d,1)\n y_emb = self.col_embed(j).unsqueeze(0).unsqueeze(2).repeat(h,1,d,1)\n z_emb = self.dep_embed(k).unsqueeze(0).unsqueeze(1).repeat(h,w,1,1)\n pos = torch.cat([x_emb,y_emb,z_emb,], dim=-1).unsqueeze(0).repeat(B, 1, 1, 1, 1)\n pos = rearrange(pos,'b h w d c -> b (h w d) c')\n return pos\n \ndef build_position_encoding(args):\n N_steps = args.hidden_dim // 2\n if args.position_embedding in ('v2', 'sine'):\n # TODO find a better way of exposing other arguments\n position_embedding = PositionEmbeddingSine(N_steps, normalize=True)\n elif args.position_embedding in ('v3', 'learned'):\n position_embedding = PositionEmbeddingLearned(N_steps)\n else:\n raise ValueError(f\"not supported {args.position_embedding}\")\n\n return position_embedding\n\n# Pos = PositionEmbeddingLearned3d()\n# x = torch.randn((8,3,32,32,1))\n# print(Pos(8,16,16,1,x))"
},
{
"path": "src/Model/RadFM/transformer_decoder.py",
"content": "\"\"\"\nCode modified from DETR tranformer:\nhttps://github.com/facebookresearch/detr\nCopyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\n\nimport copy\nfrom typing import Optional, List\nimport pickle as cp\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\n\n\nclass TransformerDecoder(nn.Module):\n\n def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):\n super().__init__()\n self.layers = _get_clones(decoder_layer, num_layers)\n self.num_layers = num_layers\n self.norm = norm\n self.return_intermediate = return_intermediate\n\n def forward(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n output = tgt\n T,B,C = memory.shape\n intermediate = []\n atten_layers = []\n for n,layer in enumerate(self.layers):\n \n residual=True\n output,ws = layer(output, memory, tgt_mask=tgt_mask,\n memory_mask=memory_mask,\n tgt_key_padding_mask=tgt_key_padding_mask,\n memory_key_padding_mask=memory_key_padding_mask,\n pos=pos, query_pos=query_pos,residual=residual)\n atten_layers.append(ws)\n if self.return_intermediate:\n intermediate.append(self.norm(output))\n if self.norm is not None:\n output = self.norm(output)\n if self.return_intermediate:\n intermediate.pop()\n intermediate.append(output)\n\n if self.return_intermediate:\n return torch.stack(intermediate)\n return output,atten_layers\n\n\n\nclass TransformerDecoderLayer(nn.Module):\n\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,\n activation=\"relu\", normalize_before=False):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.dropout3 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward_post(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n residual=True):\n q = k = self.with_pos_embed(tgt, query_pos)\n tgt2,ws = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)\n tgt = self.norm1(tgt)\n tgt2,ws = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)\n\n\n # attn_weights [B,NUM_Q,T]\n tgt = tgt + self.dropout2(tgt2)\n tgt = self.norm2(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout3(tgt2)\n tgt = self.norm3(tgt)\n return tgt,ws\n\n def forward_pre(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None):\n tgt2 = self.norm1(tgt)\n q = k = self.with_pos_embed(tgt2, query_pos)\n tgt2,ws = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,\n key_padding_mask=tgt_key_padding_mask)\n tgt = tgt + self.dropout1(tgt2)\n tgt2 = self.norm2(tgt)\n tgt2,attn_weights = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),\n key=self.with_pos_embed(memory, pos),\n value=memory, attn_mask=memory_mask,\n key_padding_mask=memory_key_padding_mask)\n tgt = tgt + self.dropout2(tgt2)\n tgt2 = self.norm3(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout3(tgt2)\n return tgt,attn_weights\n\n def forward(self, tgt, memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n residual=True):\n if self.normalize_before:\n return self.forward_pre(tgt, memory, tgt_mask, memory_mask,\n tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)\n return self.forward_post(tgt, memory, tgt_mask, memory_mask,\n tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos,residual)\n\n\ndef _get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\n\ndef _get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n"
},
{
"path": "src/Model/RadFM/utils.py",
"content": "from .blocks import ModifiedResNet,PMC_CLIP_cfg\nimport torch\nfrom torchvision import transforms\nfrom PIL import Image\nimport torch.nn as nn\ndef extend_instance(obj, mixin):\n \"\"\"Apply mixins to a class instance after creation\"\"\"\n base_cls = obj.__class__\n base_cls_name = obj.__class__.__name__\n obj.__class__ = type(\n base_cls_name, (mixin, base_cls), {}\n ) # mixin needs to go first for our forward() logic to work\n\n\ndef getattr_recursive(obj, att):\n \"\"\"\n Return nested attribute of obj\n Example: getattr_recursive(obj, 'a.b.c') is equivalent to obj.a.b.c\n \"\"\"\n if att == \"\":\n return obj\n i = att.find(\".\")\n if i < 0:\n return getattr(obj, att)\n else:\n return getattr_recursive(getattr(obj, att[:i]), att[i + 1 :])\n\n\ndef setattr_recursive(obj, att, val):\n \"\"\"\n Set nested attribute of obj\n Example: setattr_recursive(obj, 'a.b.c', val) is equivalent to obj.a.b.c = val\n \"\"\"\n if \".\" in att:\n obj = getattr_recursive(obj, \".\".join(att.split(\".\")[:-1]))\n setattr(obj, att.split(\".\")[-1], val)\n\n\n \ndef get_visual_encoder(model_str):\n \"\"\"\n Args:\n str (_type_): str_to_model_path\n Return:\n vision_model, visual_dim, img_preprocessor\n \"\"\"\n normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n img_preprocessor = transforms.Compose([ \n transforms.Resize((512,512), interpolation=Image.BICUBIC),\n transforms.ToTensor(),\n normalize,\n ])\n if 'PMC-CLIP' in model_str:\n #vision_cfg = json.load(open(model_args.visual_model_config,'r'))['vision_cfg']\n vision_cfg = PMC_CLIP_cfg()\n vision_heads = vision_cfg.width * 32 // vision_cfg.head_width\n vision_model = ModifiedResNet(\n layers=vision_cfg.layers,\n heads=vision_heads,\n output_dim = 768,\n image_size=vision_cfg.image_size,\n width=vision_cfg.width\n )\n vision_model = vision_load_pretrain(vision_model,model_str)\n vision_model = nn.Sequential(*list(vision_model.children())[:-2])\n visual_dim = 1024\n return vision_model,visual_dim,img_preprocessor\n\ndef vision_load_pretrain(resnet,model_path):\n checkpoint = torch.load(model_path, map_location='cpu') \n state_dict = checkpoint['state_dict'] \n state_dict = {k.replace('module.visual.',''): v for k, v in state_dict.items() if '.visual' in k}\n resnet.load_state_dict(state_dict)\n return resnet \n"
},
{
"path": "src/Model/RadFM/vit_3d.py",
"content": "import torch\nfrom torch import nn\n\nfrom einops import rearrange, repeat\nfrom einops.layers.torch import Rearrange\nfrom .position_encoding import PositionEmbeddingLearned3d\n\n# helpers\n\ndef pair(t):\n return t if isinstance(t, tuple) else (t, t)\n\n# classes\n\nclass PreNorm(nn.Module):\n def __init__(self, dim, fn):\n super().__init__()\n self.norm = nn.LayerNorm(dim)\n self.fn = fn\n def forward(self, x, **kwargs):\n return self.fn(self.norm(x), **kwargs)\n\nclass FeedForward(nn.Module):\n def __init__(self, dim, hidden_dim, dropout = 0.):\n super().__init__()\n self.net = nn.Sequential(\n nn.Linear(dim, hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, dim),\n nn.Dropout(dropout)\n )\n def forward(self, x):\n return self.net(x)\n\nclass Attention(nn.Module):\n def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):\n super().__init__()\n inner_dim = dim_head * heads\n project_out = not (heads == 1 and dim_head == dim)\n\n self.heads = heads\n self.scale = dim_head ** -0.5\n\n self.attend = nn.Softmax(dim = -1)\n self.dropout = nn.Dropout(dropout)\n\n self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)\n\n self.to_out = nn.Sequential(\n nn.Linear(inner_dim, dim),\n nn.Dropout(dropout)\n ) if project_out else nn.Identity()\n\n def forward(self, x):\n qkv = self.to_qkv(x).chunk(3, dim = -1)\n q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)\n\n dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale\n\n attn = self.attend(dots)\n attn = self.dropout(attn)\n\n out = torch.matmul(attn, v)\n out = rearrange(out, 'b h n d -> b n (h d)')\n return self.to_out(out)\n\nclass Transformer(nn.Module):\n def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):\n super().__init__()\n self.layers = nn.ModuleList([])\n for _ in range(depth):\n self.layers.append(nn.ModuleList([\n PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),\n PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))\n ]))\n def forward(self, x):\n for attn, ff in self.layers:\n x = attn(x) + x\n x = ff(x) + x\n return x\n\nclass ViT(nn.Module):\n def __init__(self, *, image_size, image_patch_size, frames, frame_patch_size, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):\n super().__init__()\n image_height, image_width = pair(image_size)\n patch_height, patch_width = pair(image_patch_size)\n\n assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'\n assert frames % frame_patch_size == 0, 'Frames must be divisible by frame patch size'\n\n self.patch_height = patch_height\n self.patch_width = patch_width\n self.frame_patch_size = frame_patch_size\n \n num_patches = (image_height // patch_height) * (image_width // patch_width) * (frames // frame_patch_size)\n patch_dim = channels * patch_height * patch_width * frame_patch_size\n\n assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'\n \n self.to_patch_embedding = nn.Sequential(\n Rearrange('b c (h p1) (w p2) (f pf) -> b (h w f) (p1 p2 pf c)', p1 = patch_height, p2 = patch_width, pf = frame_patch_size),\n nn.LayerNorm(patch_dim),\n nn.Linear(patch_dim, dim),\n nn.LayerNorm(dim),\n )\n\n self.pos_embedding = PositionEmbeddingLearned3d(dim // 3,(image_height // patch_height), (image_width // patch_width), (frames // frame_patch_size))\n self.dropout = nn.Dropout(emb_dropout)\n\n self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)\n\n def forward(self, video):\n B, C, H, W, D = video.shape\n x = self.to_patch_embedding(video)\n b, n, _ = x.shape\n\n pos = self.pos_embedding(B, H // self.patch_height, W // self.patch_width, D // self.frame_patch_size,x)\n x += pos\n x = self.dropout(x)\n\n x = self.transformer(x)\n return x,pos\n"
},
{
"path": "src/My_Trainer/trainer.py",
"content": "# coding=utf-8\n# Copyright 2020-present the HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nThe Trainer class, to easily train a 🤗 Transformers from scratch or finetune it on a new task.\n\"\"\"\n\nimport contextlib\nimport functools\nimport glob\nimport inspect\nimport math\nimport os\nimport random\nimport re\nimport shutil\nimport sys\nimport time\nimport warnings\nfrom collections.abc import Mapping\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union\n\nfrom tqdm.auto import tqdm\n\n\n# Integrations must be imported before ML frameworks:\n# isort: off\nfrom transformers.integrations import (\n default_hp_search_backend,\n get_reporting_integration_callbacks,\n hp_params,\n is_fairscale_available,\n is_optuna_available,\n is_ray_tune_available,\n is_sigopt_available,\n is_wandb_available,\n run_hp_search_optuna,\n run_hp_search_ray,\n run_hp_search_sigopt,\n run_hp_search_wandb,\n)\n\n# isort: on\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nfrom huggingface_hub import Repository, create_repo\nfrom packaging import version\nfrom torch import nn\nfrom torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler\nfrom torch.utils.data.distributed import DistributedSampler\n\nfrom transformers import __version__\nfrom transformers.configuration_utils import PretrainedConfig\nfrom transformers.data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator\nfrom transformers.debug_utils import DebugOption, DebugUnderflowOverflow\nfrom transformers.deepspeed import deepspeed_init, is_deepspeed_zero3_enabled\nfrom transformers.dependency_versions_check import dep_version_check\nfrom transformers.modelcard import TrainingSummary\nfrom transformers.modeling_utils import PreTrainedModel, load_sharded_checkpoint, unwrap_model\nfrom transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_MAPPING_NAMES\nfrom transformers.optimization import Adafactor, get_scheduler\nfrom transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_10, is_torch_less_than_1_11\nfrom transformers.tokenization_utils_base import PreTrainedTokenizerBase\nfrom transformers.trainer_callback import (\n CallbackHandler,\n DefaultFlowCallback,\n PrinterCallback,\n ProgressCallback,\n TrainerCallback,\n TrainerControl,\n TrainerState,\n)\nfrom transformers.trainer_pt_utils import (\n DistributedLengthGroupedSampler,\n DistributedSamplerWithLoop,\n DistributedTensorGatherer,\n IterableDatasetShard,\n LabelSmoother,\n LengthGroupedSampler,\n SequentialDistributedSampler,\n ShardSampler,\n distributed_broadcast_scalars,\n distributed_concat,\n find_batch_size,\n get_model_param_count,\n get_module_class_from_name,\n get_parameter_names,\n nested_concat,\n nested_detach,\n nested_numpify,\n nested_truncate,\n nested_xla_mesh_reduce,\n reissue_pt_warnings,\n)\nfrom transformers.trainer_utils import (\n PREFIX_CHECKPOINT_DIR,\n BestRun,\n EvalLoopOutput,\n EvalPrediction,\n FSDPOption,\n HPSearchBackend,\n HubStrategy,\n IntervalStrategy,\n PredictionOutput,\n RemoveColumnsCollator,\n ShardedDDPOption,\n TrainerMemoryTracker,\n TrainOutput,\n default_compute_objective,\n default_hp_space,\n denumpify_detensorize,\n enable_full_determinism,\n find_executable_batch_size,\n get_last_checkpoint,\n has_length,\n number_of_arguments,\n seed_worker,\n set_seed,\n speed_metrics,\n)\nfrom transformers.training_args import OptimizerNames, ParallelMode, TrainingArguments\nfrom transformers.utils import (\n CONFIG_NAME,\n SAFE_WEIGHTS_INDEX_NAME,\n SAFE_WEIGHTS_NAME,\n WEIGHTS_INDEX_NAME,\n WEIGHTS_NAME,\n can_return_loss,\n find_labels,\n get_full_repo_name,\n is_accelerate_available,\n is_apex_available,\n is_datasets_available,\n is_in_notebook,\n is_ipex_available,\n is_safetensors_available,\n is_sagemaker_dp_enabled,\n is_sagemaker_mp_enabled,\n is_torch_compile_available,\n is_torch_neuroncore_available,\n is_torch_tpu_available,\n logging,\n strtobool,\n)\nfrom transformers.utils.generic import ContextManagers\n\n\n_is_native_cpu_amp_available = is_torch_greater_or_equal_than_1_10\n\nDEFAULT_CALLBACKS = [DefaultFlowCallback]\nDEFAULT_PROGRESS_CALLBACK = ProgressCallback\n\nif is_in_notebook():\n from transformers.utils.notebook import NotebookProgressCallback\n\n DEFAULT_PROGRESS_CALLBACK = NotebookProgressCallback\n\nif is_apex_available():\n from apex import amp\n\nif is_datasets_available():\n import datasets\n\nif is_torch_tpu_available(check_device=False):\n import torch_xla.core.xla_model as xm\n import torch_xla.debug.metrics as met\n import torch_xla.distributed.parallel_loader as pl\n\nif is_fairscale_available():\n dep_version_check(\"fairscale\")\n import fairscale\n from fairscale.nn.data_parallel import FullyShardedDataParallel as FullyShardedDDP\n from fairscale.nn.data_parallel import ShardedDataParallel as ShardedDDP\n from fairscale.nn.wrap import auto_wrap\n from fairscale.optim import OSS\n from fairscale.optim.grad_scaler import ShardedGradScaler\n\n\nif is_sagemaker_mp_enabled():\n import smdistributed.modelparallel.torch as smp\n from smdistributed.modelparallel import __version__ as SMP_VERSION\n\n IS_SAGEMAKER_MP_POST_1_10 = version.parse(SMP_VERSION) >= version.parse(\"1.10\")\n\n from transformers.trainer_pt_utils import smp_forward_backward, smp_forward_only, smp_gather, smp_nested_concat\nelse:\n IS_SAGEMAKER_MP_POST_1_10 = False\n\n\nif is_safetensors_available():\n import safetensors.torch\n\n\nskip_first_batches = None\nif is_accelerate_available():\n from accelerate import __version__ as accelerate_version\n\n if version.parse(accelerate_version) >= version.parse(\"0.16\"):\n from accelerate import skip_first_batches\n\n\nif TYPE_CHECKING:\n import optuna\n\nlogger = logging.get_logger(__name__)\n\n\n# Name of the files used for checkpointing\nTRAINING_ARGS_NAME = \"training_args.bin\"\nTRAINER_STATE_NAME = \"trainer_state.json\"\nOPTIMIZER_NAME = \"optimizer.pt\"\nSCHEDULER_NAME = \"scheduler.pt\"\nSCALER_NAME = \"scaler.pt\"\n\n\nclass Trainer:\n \"\"\"\n Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.\n\n Args:\n model ([`PreTrainedModel`] or `torch.nn.Module`, *optional*):\n The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed.\n\n \n\n [`Trainer`] is optimized to work with the [`PreTrainedModel`] provided by the library. You can still use\n your own models defined as `torch.nn.Module` as long as they work the same way as the 🤗 Transformers\n models.\n\n \n\n args ([`TrainingArguments`], *optional*):\n The arguments to tweak for training. Will default to a basic instance of [`TrainingArguments`] with the\n `output_dir` set to a directory named *tmp_trainer* in the current directory if not provided.\n data_collator (`DataCollator`, *optional*):\n The function to use to form a batch from a list of elements of `train_dataset` or `eval_dataset`. Will\n default to [`default_data_collator`] if no `tokenizer` is provided, an instance of\n [`DataCollatorWithPadding`] otherwise.\n train_dataset (`torch.utils.data.Dataset` or `torch.utils.data.IterableDataset`, *optional*):\n The dataset to use for training. If it is a [`~datasets.Dataset`], columns not accepted by the\n `model.forward()` method are automatically removed.\n\n Note that if it's a `torch.utils.data.IterableDataset` with some randomization and you are training in a\n distributed fashion, your iterable dataset should either use a internal attribute `generator` that is a\n `torch.Generator` for the randomization that must be identical on all processes (and the Trainer will\n manually set the seed of this `generator` at each epoch) or have a `set_epoch()` method that internally\n sets the seed of the RNGs used.\n eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`]), *optional*):\n The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the\n `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each\n dataset prepending the dictionary key to the metric name.\n tokenizer ([`PreTrainedTokenizerBase`], *optional*):\n The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the\n maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an\n interrupted training or reuse the fine-tuned model.\n model_init (`Callable[[], PreTrainedModel]`, *optional*):\n A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start\n from a new instance of the model as given by this function.\n\n The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to\n be able to choose different architectures according to hyper parameters (such as layer count, sizes of\n inner layers, dropout probabilities etc).\n compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*):\n The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return\n a dictionary string to metric values.\n callbacks (List of [`TrainerCallback`], *optional*):\n A list of callbacks to customize the training loop. Will add those to the list of default callbacks\n detailed in [here](callback).\n\n If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method.\n optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*): A tuple\n containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your model\n and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`.\n preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):\n A function that preprocess the logits right before caching them at each evaluation step. Must take two\n tensors, the logits and the labels, and return the logits once processed as desired. The modifications made\n by this function will be reflected in the predictions received by `compute_metrics`.\n\n Note that the labels (second parameter) will be `None` if the dataset does not have them.\n\n Important attributes:\n\n - **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`]\n subclass.\n - **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the\n original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`,\n the inner model is wrapped in `DeepSpeed` and then again in `torch.nn.DistributedDataParallel`. If the inner\n model hasn't been wrapped, then `self.model_wrapped` is the same as `self.model`.\n - **is_model_parallel** -- Whether or not a model has been switched to a model parallel mode (different from\n data parallelism, this means some of the model layers are split on different GPUs).\n - **place_model_on_device** -- Whether or not to automatically place the model on the device - it will be set\n to `False` if model parallel or deepspeed is used, or if the default\n `TrainingArguments.place_model_on_device` is overridden to return `False` .\n - **is_in_train** -- Whether or not a model is currently running `train` (e.g. when `evaluate` is called while\n in `train`)\n\n \"\"\"\n\n from transformers.trainer_pt_utils import _get_learning_rate, log_metrics, metrics_format, save_metrics, save_state\n\n def __init__(\n self,\n model: Union[PreTrainedModel, nn.Module] = None,\n args: TrainingArguments = None,\n data_collator: Optional[DataCollator] = None,\n train_dataset: Optional[Dataset] = None,\n eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,\n tokenizer: Optional[PreTrainedTokenizerBase] = None,\n model_init: Optional[Callable[[], PreTrainedModel]] = None,\n compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,\n callbacks: Optional[List[TrainerCallback]] = None,\n optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),\n preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,\n ):\n if args is None:\n output_dir = \"tmp_trainer\"\n logger.info(f\"No `TrainingArguments` passed, using `output_dir={output_dir}`.\")\n args = TrainingArguments(output_dir=output_dir)\n self.args = args\n # Seed must be set before instantiating the model when using model\n enable_full_determinism(self.args.seed) if self.args.full_determinism else set_seed(self.args.seed)\n self.hp_name = None\n self.deepspeed = None\n self.is_in_train = False\n\n # memory metrics - must set up as early as possible\n self._memory_tracker = TrainerMemoryTracker(self.args.skip_memory_metrics)\n self._memory_tracker.start()\n\n # set the correct log level depending on the node\n log_level = args.get_process_log_level()\n logging.set_verbosity(log_level)\n\n # force device and distributed setup init explicitly\n args._setup_devices\n\n if model is None:\n if model_init is not None:\n self.model_init = model_init\n model = self.call_model_init()\n else:\n raise RuntimeError(\"`Trainer` requires either a `model` or `model_init` argument\")\n else:\n if model_init is not None:\n warnings.warn(\n \"`Trainer` requires either a `model` or `model_init` argument, but not both. `model_init` will\"\n \" overwrite your model when calling the `train` method. This will become a fatal error in the next\"\n \" release.\",\n FutureWarning,\n )\n self.model_init = model_init\n\n if model.__class__.__name__ in MODEL_MAPPING_NAMES:\n raise ValueError(\n f\"The model you have picked ({model.__class__.__name__}) cannot be used as is for training: it only \"\n \"computes hidden states and does not accept any labels. You should choose a model with a head \"\n \"suitable for your task like any of the `AutoModelForXxx` listed at \"\n \"https://huggingface.co/docs/transformers/model_doc/auto.\"\n )\n\n if hasattr(model, \"is_parallelizable\") and model.is_parallelizable and model.model_parallel:\n self.is_model_parallel = True\n else:\n self.is_model_parallel = False\n\n if (\n getattr(model, \"hf_device_map\", None) is not None\n and len([device for device in set(model.hf_device_map.values()) if device not in [\"cpu\", \"disk\"]]) > 1\n and not self.is_model_parallel\n ):\n self.is_model_parallel = True\n\n # warn users\n logger.info(\n \"You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set\"\n \" to `True` to avoid any unexpected behavior such as device placement mismatching.\"\n )\n\n # At this stage the model is already loaded\n if getattr(model, \"is_loaded_in_8bit\", False):\n if getattr(model, \"_is_int8_training_enabled\", False):\n logger.info(\n \"The model is loaded in 8-bit precision. To train this model you need to add additional modules\"\n \" inside the model such as adapters using `peft` library and freeze the model weights. Please\"\n \" check \"\n \" the examples in https://github.com/huggingface/peft for more details.\"\n )\n else:\n raise ValueError(\n \"The model you want to train is loaded in 8-bit precision. if you want to fine-tune an 8-bit\"\n \" model, please make sure that you have installed `bitsandbytes>=0.37.0`. \"\n )\n\n # Setup Sharded DDP training\n self.sharded_ddp = None\n if len(args.sharded_ddp) > 0:\n if args.deepspeed:\n raise ValueError(\n \"Using --sharded_ddp xxx together with --deepspeed is not possible, deactivate one of those flags.\"\n )\n if len(args.fsdp) > 0:\n raise ValueError(\n \"Using --sharded_ddp xxx together with --fsdp is not possible, deactivate one of those flags.\"\n )\n\n if args.local_rank == -1:\n raise ValueError(\"Using sharded DDP only works in distributed training.\")\n elif not is_fairscale_available():\n raise ImportError(\"Sharded DDP training requires fairscale: `pip install fairscale`.\")\n elif ShardedDDPOption.SIMPLE not in args.sharded_ddp and FullyShardedDDP is None:\n raise ImportError(\n \"Sharded DDP in a mode other than simple training requires fairscale version >= 0.3, found \"\n f\"{fairscale.__version__}. Upgrade your fairscale library: `pip install --upgrade fairscale`.\"\n )\n elif ShardedDDPOption.SIMPLE in args.sharded_ddp:\n self.sharded_ddp = ShardedDDPOption.SIMPLE\n elif ShardedDDPOption.ZERO_DP_2 in args.sharded_ddp:\n self.sharded_ddp = ShardedDDPOption.ZERO_DP_2\n elif ShardedDDPOption.ZERO_DP_3 in args.sharded_ddp:\n self.sharded_ddp = ShardedDDPOption.ZERO_DP_3\n\n self.fsdp = None\n if len(args.fsdp) > 0:\n if args.deepspeed:\n raise ValueError(\n \"Using --fsdp xxx together with --deepspeed is not possible, deactivate one of those flags.\"\n )\n if not args.fsdp_config[\"xla\"] and args.local_rank == -1:\n raise ValueError(\"Using fsdp only works in distributed training.\")\n\n # dep_version_check(\"torch>=1.12.0\")\n # Would have to update setup.py with torch>=1.12.0\n # which isn't ideally given that it will force people not using FSDP to also use torch>=1.12.0\n # below is the current alternative.\n if version.parse(version.parse(torch.__version__).base_version) < version.parse(\"1.12.0\"):\n raise ValueError(\"FSDP requires PyTorch >= 1.12.0\")\n\n from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch, ShardingStrategy\n\n if FSDPOption.FULL_SHARD in args.fsdp:\n self.fsdp = ShardingStrategy.FULL_SHARD\n elif FSDPOption.SHARD_GRAD_OP in args.fsdp:\n self.fsdp = ShardingStrategy.SHARD_GRAD_OP\n elif FSDPOption.NO_SHARD in args.fsdp:\n self.fsdp = ShardingStrategy.NO_SHARD\n\n self.backward_prefetch = BackwardPrefetch.BACKWARD_PRE\n if \"backward_prefetch\" in self.args.fsdp_config and \"backward_pos\" not in self.backward_prefetch:\n self.backward_prefetch = BackwardPrefetch.BACKWARD_POST\n\n self.forward_prefetch = False\n if self.args.fsdp_config.get(\"forward_prefect\", False):\n self.forward_prefetch = True\n\n self.limit_all_gathers = False\n if self.args.fsdp_config.get(\"limit_all_gathers\", False):\n self.limit_all_gathers = True\n\n # one place to sort out whether to place the model on device or not\n # postpone switching model to cuda when:\n # 1. MP - since we are trying to fit a much bigger than 1 gpu model\n # 2. fp16-enabled DeepSpeed loads the model in half the size and it doesn't need .to() anyway,\n # and we only use deepspeed for training at the moment\n # 3. full bf16 or fp16 eval - since the model needs to be cast to the right dtype first\n # 4. Sharded DDP - same as MP\n # 5. FSDP - same as MP\n self.place_model_on_device = args.place_model_on_device\n if (\n self.is_model_parallel\n or args.deepspeed\n or ((args.fp16_full_eval or args.bf16_full_eval) and not args.do_train)\n or (self.sharded_ddp in [ShardedDDPOption.ZERO_DP_2, ShardedDDPOption.ZERO_DP_3])\n or (self.fsdp is not None)\n ):\n self.place_model_on_device = False\n\n default_collator = default_data_collator if tokenizer is None else DataCollatorWithPadding(tokenizer)\n self.data_collator = data_collator if data_collator is not None else default_collator\n self.train_dataset = train_dataset\n self.eval_dataset = eval_dataset\n self.tokenizer = tokenizer\n\n if self.place_model_on_device and not getattr(model, \"is_loaded_in_8bit\", False):\n self._move_model_to_device(model, args.device)\n\n # Force n_gpu to 1 to avoid DataParallel as MP will manage the GPUs\n if self.is_model_parallel:\n self.args._n_gpu = 1\n\n # later use `self.model is self.model_wrapped` to check if it's wrapped or not\n self.model_wrapped = model\n self.model = model\n\n self.compute_metrics = compute_metrics\n self.preprocess_logits_for_metrics = preprocess_logits_for_metrics\n self.optimizer, self.lr_scheduler = optimizers\n if model_init is not None and (self.optimizer is not None or self.lr_scheduler is not None):\n raise RuntimeError(\n \"Passing a `model_init` is incompatible with providing the `optimizers` argument. \"\n \"You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method.\"\n )\n if is_torch_tpu_available() and self.optimizer is not None:\n for param in self.model.parameters():\n model_device = param.device\n break\n for param_group in self.optimizer.param_groups:\n if len(param_group[\"params\"]) > 0:\n optimizer_device = param_group[\"params\"][0].device\n break\n if model_device != optimizer_device:\n raise ValueError(\n \"The model and the optimizer parameters are not on the same device, which probably means you\"\n \" created an optimizer around your model **before** putting on the device and passing it to the\"\n \" `Trainer`. Make sure the lines `import torch_xla.core.xla_model as xm` and\"\n \" `model.to(xm.xla_device())` is performed before the optimizer creation in your script.\"\n )\n if ((self.sharded_ddp is not None) or args.deepspeed or (self.fsdp is not None)) and (\n self.optimizer is not None or self.lr_scheduler is not None\n ):\n raise RuntimeError(\n \"Passing `optimizers` is not allowed if Fairscale, Deepspeed or PyTorch FSDP is enabled.\"\n \"You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method.\"\n )\n default_callbacks = DEFAULT_CALLBACKS + get_reporting_integration_callbacks(self.args.report_to)\n callbacks = default_callbacks if callbacks is None else default_callbacks + callbacks\n self.callback_handler = CallbackHandler(\n callbacks, self.model, self.tokenizer, self.optimizer, self.lr_scheduler\n )\n self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK)\n\n # Will be set to True by `self._setup_loggers()` on first call to `self.log()`.\n self._loggers_initialized = False\n\n # Create clone of distant repo and output directory if needed\n if self.args.push_to_hub:\n self.init_git_repo(at_init=True)\n # In case of pull, we need to make sure every process has the latest.\n if is_torch_tpu_available():\n xm.rendezvous(\"init git repo\")\n elif args.local_rank != -1:\n dist.barrier()\n\n if self.args.should_save:\n os.makedirs(self.args.output_dir, exist_ok=True)\n\n if not callable(self.data_collator) and callable(getattr(self.data_collator, \"collate_batch\", None)):\n raise ValueError(\"The `data_collator` should be a simple callable (function, class with `__call__`).\")\n\n if args.max_steps > 0:\n logger.info(\"max_steps is given, it will override any value given in num_train_epochs\")\n\n if train_dataset is not None and not has_length(train_dataset) and args.max_steps <= 0:\n raise ValueError(\n \"The train_dataset does not implement __len__, max_steps has to be specified. \"\n \"The number of steps needs to be known in advance for the learning rate scheduler.\"\n )\n\n if (\n train_dataset is not None\n and isinstance(train_dataset, torch.utils.data.IterableDataset)\n and args.group_by_length\n ):\n raise ValueError(\"the `--group_by_length` option is only available for `Dataset`, not `IterableDataset\")\n\n self._signature_columns = None\n\n # Mixed precision setup\n self.use_apex = False\n self.use_cuda_amp = False\n self.use_cpu_amp = False\n\n # Mixed precision setup for SageMaker Model Parallel\n if is_sagemaker_mp_enabled():\n # BF16 + model parallelism in SageMaker: currently not supported, raise an error\n if args.bf16:\n raise ValueError(\"SageMaker Model Parallelism does not support BF16 yet. Please use FP16 instead \")\n\n if IS_SAGEMAKER_MP_POST_1_10:\n # When there's mismatch between SMP config and trainer argument, use SMP config as truth\n if args.fp16 != smp.state.cfg.fp16:\n logger.warning(\n f\"FP16 provided in SM_HP_MP_PARAMETERS is {smp.state.cfg.fp16},\"\n f\"but FP16 provided in trainer argument is {args.fp16},\"\n f\"setting to {smp.state.cfg.fp16}\"\n )\n args.fp16 = smp.state.cfg.fp16\n else:\n # smp < 1.10 does not support fp16 in trainer.\n if hasattr(smp.state.cfg, \"fp16\"):\n logger.warning(\n f\"FP16 provided in SM_HP_MP_PARAMETERS is {smp.state.cfg.fp16}, \"\n \"but SageMaker Model Parallelism < 1.10 does not support FP16 in trainer.\"\n )\n\n if args.fp16 or args.bf16:\n if args.half_precision_backend == \"auto\":\n if args.device == torch.device(\"cpu\"):\n if args.fp16:\n raise ValueError(\"Tried to use `fp16` but it is not supported on cpu\")\n elif _is_native_cpu_amp_available:\n args.half_precision_backend = \"cpu_amp\"\n else:\n raise ValueError(\"Tried to use cpu amp but native cpu amp is not available\")\n else:\n args.half_precision_backend = \"cuda_amp\"\n\n logger.info(f\"Using {args.half_precision_backend} half precision backend\")\n\n self.do_grad_scaling = False\n if (args.fp16 or args.bf16) and not (args.deepspeed or is_sagemaker_mp_enabled()):\n # deepspeed and SageMaker Model Parallel manage their own half precision\n if args.half_precision_backend == \"cuda_amp\":\n self.use_cuda_amp = True\n self.amp_dtype = torch.float16 if args.fp16 else torch.bfloat16\n # bf16 does not need grad scaling\n self.do_grad_scaling = self.amp_dtype == torch.float16\n if self.do_grad_scaling:\n if self.sharded_ddp is not None:\n self.scaler = ShardedGradScaler()\n elif self.fsdp is not None:\n from torch.distributed.fsdp.sharded_grad_scaler import (\n ShardedGradScaler as FSDPShardedGradScaler,\n )\n\n self.scaler = FSDPShardedGradScaler()\n elif is_torch_tpu_available():\n from torch_xla.amp import GradScaler\n\n self.scaler = GradScaler()\n else:\n self.scaler = torch.cuda.amp.GradScaler()\n elif args.half_precision_backend == \"cpu_amp\":\n self.use_cpu_amp = True\n self.amp_dtype = torch.bfloat16\n else:\n if not is_apex_available():\n raise ImportError(\n \"Using FP16 with APEX but APEX is not installed, please refer to\"\n \" https://www.github.com/nvidia/apex.\"\n )\n self.use_apex = True\n\n # FP16 + model parallelism in SageMaker: gradient clipping does not work for now so we raise a helpful error.\n if (\n is_sagemaker_mp_enabled()\n and self.use_cuda_amp\n and args.max_grad_norm is not None\n and args.max_grad_norm > 0\n ):\n raise ValueError(\n \"SageMaker Model Parallelism in mixed precision mode does not support gradient clipping yet. Pass \"\n \"along 'max_grad_norm': 0 in your hyperparameters.\"\n )\n\n # Label smoothing\n if self.args.label_smoothing_factor != 0:\n self.label_smoother = LabelSmoother(epsilon=self.args.label_smoothing_factor)\n else:\n self.label_smoother = None\n\n self.state = TrainerState(\n is_local_process_zero=self.is_local_process_zero(),\n is_world_process_zero=self.is_world_process_zero(),\n )\n\n self.control = TrainerControl()\n # Internal variable to count flos in each process, will be accumulated in `self.state.total_flos` then\n # returned to 0 every time flos need to be logged\n self.current_flos = 0\n self.hp_search_backend = None\n self.use_tune_checkpoints = False\n default_label_names = find_labels(self.model.__class__)\n self.label_names = default_label_names if self.args.label_names is None else self.args.label_names\n self.can_return_loss = can_return_loss(self.model.__class__)\n self.control = self.callback_handler.on_init_end(self.args, self.state, self.control)\n\n # Internal variables to keep track of the original batch size\n self._train_batch_size = args.train_batch_size\n\n # very last\n self._memory_tracker.stop_and_update_metrics()\n\n # torch.compile\n if args.torch_compile and not is_torch_compile_available():\n raise RuntimeError(\"Using torch.compile requires PyTorch 2.0 or higher.\")\n\n def add_callback(self, callback):\n \"\"\"\n Add a callback to the current list of [`~transformer.TrainerCallback`].\n\n Args:\n callback (`type` or [`~transformer.TrainerCallback`]):\n A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the\n first case, will instantiate a member of that class.\n \"\"\"\n self.callback_handler.add_callback(callback)\n\n def pop_callback(self, callback):\n \"\"\"\n Remove a callback from the current list of [`~transformer.TrainerCallback`] and returns it.\n\n If the callback is not found, returns `None` (and no error is raised).\n\n Args:\n callback (`type` or [`~transformer.TrainerCallback`]):\n A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the\n first case, will pop the first member of that class found in the list of callbacks.\n\n Returns:\n [`~transformer.TrainerCallback`]: The callback removed, if found.\n \"\"\"\n return self.callback_handler.pop_callback(callback)\n\n def remove_callback(self, callback):\n \"\"\"\n Remove a callback from the current list of [`~transformer.TrainerCallback`].\n\n Args:\n callback (`type` or [`~transformer.TrainerCallback`]):\n A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the\n first case, will remove the first member of that class found in the list of callbacks.\n \"\"\"\n self.callback_handler.remove_callback(callback)\n\n def _move_model_to_device(self, model, device):\n model = model.to(device)\n # Moving a model to an XLA device disconnects the tied weights, so we have to retie them.\n if self.args.parallel_mode == ParallelMode.TPU and hasattr(model, \"tie_weights\"):\n model.tie_weights()\n\n def _set_signature_columns_if_needed(self):\n if self._signature_columns is None:\n # Inspect model forward signature to keep only the arguments it accepts.\n signature = inspect.signature(self.model.forward)\n self._signature_columns = list(signature.parameters.keys())\n # Labels may be named label or label_ids, the default data collator handles that.\n self._signature_columns += list(set([\"label\", \"label_ids\"] + self.label_names))\n\n def _remove_unused_columns(self, dataset: \"datasets.Dataset\", description: Optional[str] = None):\n if not self.args.remove_unused_columns:\n return dataset\n self._set_signature_columns_if_needed()\n signature_columns = self._signature_columns\n\n ignored_columns = list(set(dataset.column_names) - set(signature_columns))\n if len(ignored_columns) > 0:\n dset_description = \"\" if description is None else f\"in the {description} set\"\n logger.info(\n f\"The following columns {dset_description} don't have a corresponding argument in \"\n f\"`{self.model.__class__.__name__}.forward` and have been ignored: {', '.join(ignored_columns)}.\"\n f\" If {', '.join(ignored_columns)} are not expected by `{self.model.__class__.__name__}.forward`, \"\n \" you can safely ignore this message.\"\n )\n\n columns = [k for k in signature_columns if k in dataset.column_names]\n\n if version.parse(datasets.__version__) < version.parse(\"1.4.0\"):\n dataset.set_format(\n type=dataset.format[\"type\"], columns=columns, format_kwargs=dataset.format[\"format_kwargs\"]\n )\n return dataset\n else:\n return dataset.remove_columns(ignored_columns)\n\n def _get_collator_with_removed_columns(\n self, data_collator: Callable, description: Optional[str] = None\n ) -> Callable:\n \"\"\"Wrap the data collator in a callable removing unused columns.\"\"\"\n if not self.args.remove_unused_columns:\n return data_collator\n self._set_signature_columns_if_needed()\n signature_columns = self._signature_columns\n\n remove_columns_collator = RemoveColumnsCollator(\n data_collator=data_collator,\n signature_columns=signature_columns,\n logger=logger,\n description=description,\n model_name=self.model.__class__.__name__,\n )\n return remove_columns_collator\n\n def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:\n if self.train_dataset is None or not has_length(self.train_dataset):\n return None\n\n generator = None\n if self.args.world_size <= 1:\n generator = torch.Generator()\n # for backwards compatibility, we generate a seed here (which is sampled from a generator seeded with\n # `args.seed`) if data_seed isn't provided.\n # Further on in this method, we default to `args.seed` instead.\n if self.args.data_seed is None:\n seed = int(torch.empty((), dtype=torch.int64).random_().item())\n else:\n seed = self.args.data_seed\n generator.manual_seed(seed)\n\n seed = self.args.data_seed if self.args.data_seed is not None else self.args.seed\n\n # Build the sampler.\n if self.args.group_by_length:\n if is_datasets_available() and isinstance(self.train_dataset, datasets.Dataset):\n lengths = (\n self.train_dataset[self.args.length_column_name]\n if self.args.length_column_name in self.train_dataset.column_names\n else None\n )\n else:\n lengths = None\n model_input_name = self.tokenizer.model_input_names[0] if self.tokenizer is not None else None\n if self.args.world_size <= 1:\n return LengthGroupedSampler(\n self.args.train_batch_size * self.args.gradient_accumulation_steps,\n dataset=self.train_dataset,\n lengths=lengths,\n model_input_name=model_input_name,\n generator=generator,\n )\n else:\n return DistributedLengthGroupedSampler(\n self.args.train_batch_size * self.args.gradient_accumulation_steps,\n dataset=self.train_dataset,\n num_replicas=self.args.world_size,\n rank=self.args.process_index,\n lengths=lengths,\n model_input_name=model_input_name,\n seed=seed,\n )\n\n else:\n if self.args.world_size <= 1:\n return RandomSampler(self.train_dataset, generator=generator)\n elif (\n self.args.parallel_mode in [ParallelMode.TPU, ParallelMode.SAGEMAKER_MODEL_PARALLEL]\n and not self.args.dataloader_drop_last\n ):\n # Use a loop for TPUs when drop_last is False to have all batches have the same size.\n return DistributedSamplerWithLoop(\n self.train_dataset,\n batch_size=self.args.per_device_train_batch_size,\n num_replicas=self.args.world_size,\n rank=self.args.process_index,\n seed=seed,\n )\n else:\n return DistributedSampler(\n self.train_dataset,\n num_replicas=self.args.world_size,\n rank=self.args.process_index,\n seed=seed,\n )\n\n def get_train_dataloader(self) -> DataLoader:\n \"\"\"\n Returns the training [`~torch.utils.data.DataLoader`].\n\n Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed\n training if necessary) otherwise.\n\n Subclass and override this method if you want to inject some custom behavior.\n \"\"\"\n if self.train_dataset is None:\n raise ValueError(\"Trainer: training requires a train_dataset.\")\n\n train_dataset = self.train_dataset\n data_collator = self.data_collator\n if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):\n train_dataset = self._remove_unused_columns(train_dataset, description=\"training\")\n else:\n data_collator = self._get_collator_with_removed_columns(data_collator, description=\"training\")\n\n if isinstance(train_dataset, torch.utils.data.IterableDataset):\n if self.args.world_size > 1:\n train_dataset = IterableDatasetShard(\n train_dataset,\n batch_size=self._train_batch_size,\n drop_last=self.args.dataloader_drop_last,\n num_processes=self.args.world_size,\n process_index=self.args.process_index,\n )\n\n return DataLoader(\n train_dataset,\n batch_size=self._train_batch_size,\n collate_fn=data_collator,\n num_workers=self.args.dataloader_num_workers,\n pin_memory=self.args.dataloader_pin_memory,\n )\n ### 吴超逸加 ###\n if self.args.data_sampler != None:\n #print(self.args.world_size,self.args.process_index,self.args.batch_size_2D,self.args.batch_size_3D)\n train_sampler = self.args.data_sampler( \n dataset=self.train_dataset,\n num_replicas=self.args.world_size,\n rank=self.args.process_index,\n batch_size_2D = self.args.batch_size_2D,\n batch_size_3D = self.args.batch_size_3D,\n )\n return DataLoader(\n train_dataset,\n batch_sampler=train_sampler,\n collate_fn=data_collator,\n num_workers=self.args.dataloader_num_workers,\n pin_memory=self.args.dataloader_pin_memory,\n worker_init_fn=seed_worker,\n )\n else:\n ### 吴超逸加 ###\n train_sampler = self._get_train_sampler()\n return DataLoader(\n train_dataset,\n batch_size=self._train_batch_size,\n sampler=train_sampler,\n collate_fn=data_collator,\n drop_last=self.args.dataloader_drop_last,\n num_workers=self.args.dataloader_num_workers,\n pin_memory=self.args.dataloader_pin_memory,\n worker_init_fn=seed_worker,\n )\n\n def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.utils.data.Sampler]:\n # Deprecated code\n if self.args.use_legacy_prediction_loop:\n if is_torch_tpu_available():\n return SequentialDistributedSampler(\n eval_dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()\n )\n elif is_sagemaker_mp_enabled():\n return SequentialDistributedSampler(\n eval_dataset,\n num_replicas=smp.dp_size(),\n rank=smp.dp_rank(),\n batch_size=self.args.per_device_eval_batch_size,\n )\n elif self.args.local_rank != -1:\n return SequentialDistributedSampler(eval_dataset)\n else:\n return SequentialSampler(eval_dataset)\n\n if self.args.world_size <= 1:\n return SequentialSampler(eval_dataset)\n else:\n return ShardSampler(\n eval_dataset,\n batch_size=self.args.per_device_eval_batch_size,\n num_processes=self.args.world_size,\n process_index=self.args.process_index,\n )\n\n def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:\n \"\"\"\n Returns the evaluation [`~torch.utils.data.DataLoader`].\n\n Subclass and override this method if you want to inject some custom behavior.\n\n Args:\n eval_dataset (`torch.utils.data.Dataset`, *optional*):\n If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted\n by the `model.forward()` method are automatically removed. It must implement `__len__`.\n \"\"\"\n if eval_dataset is None and self.eval_dataset is None:\n raise ValueError(\"Trainer: evaluation requires an eval_dataset.\")\n eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset\n data_collator = self.data_collator\n\n if is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):\n eval_dataset = self._remove_unused_columns(eval_dataset, description=\"evaluation\")\n else:\n data_collator = self._get_collator_with_removed_columns(data_collator, description=\"evaluation\")\n\n if isinstance(eval_dataset, torch.utils.data.IterableDataset):\n if self.args.world_size > 1:\n eval_dataset = IterableDatasetShard(\n eval_dataset,\n batch_size=self.args.per_device_eval_batch_size,\n drop_last=self.args.dataloader_drop_last,\n num_processes=self.args.world_size,\n process_index=self.args.process_index,\n )\n return DataLoader(\n eval_dataset,\n batch_size=self.args.eval_batch_size,\n collate_fn=data_collator,\n num_workers=self.args.dataloader_num_workers,\n pin_memory=self.args.dataloader_pin_memory,\n )\n\n eval_sampler = self._get_eval_sampler(eval_dataset)\n\n return DataLoader(\n eval_dataset,\n sampler=eval_sampler,\n batch_size=self.args.eval_batch_size,\n collate_fn=data_collator,\n drop_last=self.args.dataloader_drop_last,\n num_workers=self.args.dataloader_num_workers,\n pin_memory=self.args.dataloader_pin_memory,\n )\n\n def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:\n \"\"\"\n Returns the test [`~torch.utils.data.DataLoader`].\n\n Subclass and override this method if you want to inject some custom behavior.\n\n Args:\n test_dataset (`torch.utils.data.Dataset`, *optional*):\n The test dataset to use. If it is a [`~datasets.Dataset`], columns not accepted by the\n `model.forward()` method are automatically removed. It must implement `__len__`.\n \"\"\"\n data_collator = self.data_collator\n\n if is_datasets_available() and isinstance(test_dataset, datasets.Dataset):\n test_dataset = self._remove_unused_columns(test_dataset, description=\"test\")\n else:\n data_collator = self._get_collator_with_removed_columns(data_collator, description=\"test\")\n\n if isinstance(test_dataset, torch.utils.data.IterableDataset):\n if self.args.world_size > 1:\n test_dataset = IterableDatasetShard(\n test_dataset,\n batch_size=self.args.eval_batch_size,\n drop_last=self.args.dataloader_drop_last,\n num_processes=self.args.world_size,\n process_index=self.args.process_index,\n )\n return DataLoader(\n test_dataset,\n batch_size=self.args.eval_batch_size,\n collate_fn=data_collator,\n num_workers=self.args.dataloader_num_workers,\n pin_memory=self.args.dataloader_pin_memory,\n )\n\n test_sampler = self._get_eval_sampler(test_dataset)\n\n # We use the same batch_size as for eval.\n return DataLoader(\n test_dataset,\n sampler=test_sampler,\n batch_size=self.args.eval_batch_size,\n collate_fn=data_collator,\n drop_last=self.args.dataloader_drop_last,\n num_workers=self.args.dataloader_num_workers,\n pin_memory=self.args.dataloader_pin_memory,\n )\n\n def create_optimizer_and_scheduler(self, num_training_steps: int):\n \"\"\"\n Setup the optimizer and the learning rate scheduler.\n\n We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the\n Trainer's init through `optimizers`, or subclass and override this method (or `create_optimizer` and/or\n `create_scheduler`) in a subclass.\n \"\"\"\n self.create_optimizer()\n if IS_SAGEMAKER_MP_POST_1_10 and smp.state.cfg.fp16:\n # If smp >= 1.10 and fp16 is enabled, we unwrap the optimizer\n optimizer = self.optimizer.optimizer\n else:\n optimizer = self.optimizer\n self.create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer)\n\n def create_optimizer(self):\n \"\"\"\n Setup the optimizer.\n\n We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the\n Trainer's init through `optimizers`, or subclass and override this method in a subclass.\n \"\"\"\n opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model\n\n if self.optimizer is None:\n decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)\n decay_parameters = [name for name in decay_parameters if \"bias\" not in name]\n optimizer_grouped_parameters = [\n {\n \"params\": [\n p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)\n ],\n \"weight_decay\": self.args.weight_decay,\n },\n {\n \"params\": [\n p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)\n ],\n \"weight_decay\": 0.0,\n },\n ]\n\n optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(self.args)\n\n if self.sharded_ddp == ShardedDDPOption.SIMPLE:\n self.optimizer = OSS(\n params=optimizer_grouped_parameters,\n optim=optimizer_cls,\n **optimizer_kwargs,\n )\n else:\n self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)\n if optimizer_cls.__name__ == \"Adam8bit\":\n import bitsandbytes\n\n manager = bitsandbytes.optim.GlobalOptimManager.get_instance()\n\n skipped = 0\n for module in opt_model.modules():\n if isinstance(module, nn.Embedding):\n skipped += sum({p.data_ptr(): p.numel() for p in module.parameters()}.values())\n print(f\"skipped {module}: {skipped/2**20}M params\")\n manager.register_module_override(module, \"weight\", {\"optim_bits\": 32})\n logger.debug(f\"bitsandbytes: will optimize {module} in fp32\")\n print(f\"skipped: {skipped/2**20}M params\")\n\n if is_sagemaker_mp_enabled():\n self.optimizer = smp.DistributedOptimizer(self.optimizer)\n\n return self.optimizer\n\n @staticmethod\n def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> Tuple[Any, Any]:\n \"\"\"\n Returns the optimizer class and optimizer parameters based on the training arguments.\n\n Args:\n args (`transformers.training_args.TrainingArguments`):\n The training arguments for the training session.\n\n \"\"\"\n\n # parse args.optim_args\n optim_args = {}\n if args.optim_args:\n for mapping in args.optim_args.replace(\" \", \"\").split(\",\"):\n key, value = mapping.split(\"=\")\n optim_args[key] = value\n\n optimizer_kwargs = {\"lr\": args.learning_rate}\n\n adam_kwargs = {\n \"betas\": (args.adam_beta1, args.adam_beta2),\n \"eps\": args.adam_epsilon,\n }\n if args.optim == OptimizerNames.ADAFACTOR:\n optimizer_cls = Adafactor\n optimizer_kwargs.update({\"scale_parameter\": False, \"relative_step\": False})\n elif args.optim == OptimizerNames.ADAMW_HF:\n from .optimization import AdamW\n\n optimizer_cls = AdamW\n optimizer_kwargs.update(adam_kwargs)\n elif args.optim in [OptimizerNames.ADAMW_TORCH, OptimizerNames.ADAMW_TORCH_FUSED]:\n from torch.optim import AdamW\n\n optimizer_cls = AdamW\n optimizer_kwargs.update(adam_kwargs)\n if args.optim == OptimizerNames.ADAMW_TORCH_FUSED:\n optimizer_kwargs.update({\"fused\": True})\n elif args.optim == OptimizerNames.ADAMW_TORCH_XLA:\n try:\n from torch_xla.amp.syncfree import AdamW\n\n optimizer_cls = AdamW\n optimizer_kwargs.update(adam_kwargs)\n except ImportError:\n raise ValueError(\"Trainer failed to import syncfree AdamW from torch_xla.\")\n elif args.optim == OptimizerNames.ADAMW_APEX_FUSED:\n try:\n from apex.optimizers import FusedAdam\n\n optimizer_cls = FusedAdam\n optimizer_kwargs.update(adam_kwargs)\n except ImportError:\n raise ValueError(\"Trainer tried to instantiate apex FusedAdam but apex is not installed!\")\n elif args.optim == OptimizerNames.ADAMW_BNB:\n try:\n from bitsandbytes.optim import Adam8bit\n\n optimizer_cls = Adam8bit\n optimizer_kwargs.update(adam_kwargs)\n except ImportError:\n raise ValueError(\"Trainer tried to instantiate bnb Adam8bit but bnb is not installed!\")\n elif args.optim == OptimizerNames.ADAMW_ANYPRECISION:\n try:\n from torchdistx.optimizers import AnyPrecisionAdamW\n\n optimizer_cls = AnyPrecisionAdamW\n optimizer_kwargs.update(adam_kwargs)\n\n # TODO Change dtypes back to M=FP32, Var = BF16, Kahan = False once they can be cast together in torchdistx.\n optimizer_kwargs.update(\n {\n \"use_kahan_summation\": strtobool(optim_args.get(\"use_kahan_summation\", \"False\")),\n \"momentum_dtype\": getattr(torch, optim_args.get(\"momentum_dtype\", \"float32\")),\n \"variance_dtype\": getattr(torch, optim_args.get(\"variance_dtype\", \"float32\")),\n \"compensation_buffer_dtype\": getattr(\n torch, optim_args.get(\"compensation_buffer_dtype\", \"bfloat16\")\n ),\n }\n )\n except ImportError:\n raise ValueError(\"Please install https://github.com/pytorch/torchdistx\")\n elif args.optim == OptimizerNames.SGD:\n optimizer_cls = torch.optim.SGD\n elif args.optim == OptimizerNames.ADAGRAD:\n optimizer_cls = torch.optim.Adagrad\n else:\n raise ValueError(f\"Trainer cannot instantiate unsupported optimizer: {args.optim}\")\n return optimizer_cls, optimizer_kwargs\n\n def create_scheduler(self, num_training_steps: int, optimizer: torch.optim.Optimizer = None):\n \"\"\"\n Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or\n passed as an argument.\n\n Args:\n num_training_steps (int): The number of training steps to do.\n \"\"\"\n if self.lr_scheduler is None:\n self.lr_scheduler = get_scheduler(\n self.args.lr_scheduler_type,\n optimizer=self.optimizer if optimizer is None else optimizer,\n num_warmup_steps=self.args.get_warmup_steps(num_training_steps),\n num_training_steps=num_training_steps,\n )\n return self.lr_scheduler\n\n def num_examples(self, dataloader: DataLoader) -> int:\n \"\"\"\n Helper to get number of samples in a [`~torch.utils.data.DataLoader`] by accessing its dataset. When\n dataloader.dataset does not exist or has no length, estimates as best it can\n \"\"\"\n try:\n dataset = dataloader.dataset\n # Special case for IterableDatasetShard, we need to dig deeper\n if isinstance(dataset, IterableDatasetShard):\n return len(dataloader.dataset.dataset)\n return len(dataloader.dataset)\n except (NameError, AttributeError, TypeError): # no dataset or length, estimate by length of dataloader\n return len(dataloader) * self.args.per_device_train_batch_size\n\n def _hp_search_setup(self, trial: Union[\"optuna.Trial\", Dict[str, Any]]):\n \"\"\"HP search setup code\"\"\"\n self._trial = trial\n\n if self.hp_search_backend is None or trial is None:\n return\n if self.hp_search_backend == HPSearchBackend.OPTUNA:\n params = self.hp_space(trial)\n elif self.hp_search_backend == HPSearchBackend.RAY:\n params = trial\n params.pop(\"wandb\", None)\n elif self.hp_search_backend == HPSearchBackend.SIGOPT:\n params = {k: int(v) if isinstance(v, str) else v for k, v in trial.assignments.items()}\n elif self.hp_search_backend == HPSearchBackend.WANDB:\n params = trial\n\n for key, value in params.items():\n if not hasattr(self.args, key):\n logger.warning(\n f\"Trying to set {key} in the hyperparameter search but there is no corresponding field in\"\n \" `TrainingArguments`.\"\n )\n continue\n old_attr = getattr(self.args, key, None)\n # Casting value to the proper type\n if old_attr is not None:\n value = type(old_attr)(value)\n setattr(self.args, key, value)\n if self.hp_search_backend == HPSearchBackend.OPTUNA:\n logger.info(f\"Trial: {trial.params}\")\n if self.hp_search_backend == HPSearchBackend.SIGOPT:\n logger.info(f\"SigOpt Assignments: {trial.assignments}\")\n if self.hp_search_backend == HPSearchBackend.WANDB:\n logger.info(f\"W&B Sweep parameters: {trial}\")\n if self.args.deepspeed:\n # Rebuild the deepspeed config to reflect the updated training parameters\n from transformers.deepspeed import HfTrainerDeepSpeedConfig\n\n self.args.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.args.deepspeed)\n self.args.hf_deepspeed_config.trainer_config_process(self.args)\n\n def _report_to_hp_search(self, trial: Union[\"optuna.Trial\", Dict[str, Any]], step: int, metrics: Dict[str, float]):\n if self.hp_search_backend is None or trial is None:\n return\n self.objective = self.compute_objective(metrics.copy())\n if self.hp_search_backend == HPSearchBackend.OPTUNA:\n import optuna\n\n trial.report(self.objective, step)\n if trial.should_prune():\n self.callback_handler.on_train_end(self.args, self.state, self.control)\n raise optuna.TrialPruned()\n elif self.hp_search_backend == HPSearchBackend.RAY:\n from ray import tune\n\n if self.control.should_save:\n self._tune_save_checkpoint()\n tune.report(objective=self.objective, **metrics)\n\n def _tune_save_checkpoint(self):\n from ray import tune\n\n if not self.use_tune_checkpoints:\n return\n with tune.checkpoint_dir(step=self.state.global_step) as checkpoint_dir:\n output_dir = os.path.join(checkpoint_dir, f\"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}\")\n self.save_model(output_dir, _internal_call=True)\n if self.args.should_save:\n self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))\n torch.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))\n torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))\n\n def call_model_init(self, trial=None):\n model_init_argcount = number_of_arguments(self.model_init)\n if model_init_argcount == 0:\n model = self.model_init()\n elif model_init_argcount == 1:\n model = self.model_init(trial)\n else:\n raise RuntimeError(\"model_init should have 0 or 1 argument.\")\n\n if model is None:\n raise RuntimeError(\"model_init should not return None.\")\n\n return model\n\n def torch_jit_model_eval(self, model, dataloader, training=False):\n if not training:\n if dataloader is None:\n logger.warning(\"failed to use PyTorch jit mode due to current dataloader is none.\")\n return model\n example_batch = next(iter(dataloader))\n example_batch = self._prepare_inputs(example_batch)\n try:\n jit_model = model.eval()\n with ContextManagers([self.autocast_smart_context_manager(cache_enabled=False), torch.no_grad()]):\n if version.parse(version.parse(torch.__version__).base_version) >= version.parse(\"1.14.0\"):\n if isinstance(example_batch, dict):\n jit_model = torch.jit.trace(jit_model, example_kwarg_inputs=example_batch, strict=False)\n else:\n jit_model = torch.jit.trace(\n jit_model,\n example_kwarg_inputs={key: example_batch[key] for key in example_batch},\n strict=False,\n )\n else:\n jit_inputs = []\n for key in example_batch:\n example_tensor = torch.ones_like(example_batch[key])\n jit_inputs.append(example_tensor)\n jit_inputs = tuple(jit_inputs)\n jit_model = torch.jit.trace(jit_model, jit_inputs, strict=False)\n jit_model = torch.jit.freeze(jit_model)\n with torch.no_grad():\n jit_model(**example_batch)\n jit_model(**example_batch)\n model = jit_model\n self.use_cpu_amp = False\n self.use_cuda_amp = False\n except (RuntimeError, TypeError, ValueError, NameError, IndexError) as e:\n logger.warning(f\"failed to use PyTorch jit mode due to: {e}.\")\n\n return model\n\n def ipex_optimize_model(self, model, training=False, dtype=torch.float32):\n if not is_ipex_available():\n raise ImportError(\n \"Using IPEX but IPEX is not installed or IPEX's version does not match current PyTorch, please refer\"\n \" to https://github.com/intel/intel-extension-for-pytorch.\"\n )\n\n import intel_extension_for_pytorch as ipex\n\n if not training:\n model.eval()\n dtype = torch.bfloat16 if not self.is_in_train and self.args.bf16_full_eval else dtype\n # conv_bn_folding is disabled as it fails in symbolic tracing, resulting in ipex warnings\n model = ipex.optimize(model, dtype=dtype, level=\"O1\", conv_bn_folding=False, inplace=not self.is_in_train)\n else:\n if not model.training:\n model.train()\n model, self.optimizer = ipex.optimize(\n model, dtype=dtype, optimizer=self.optimizer, inplace=True, level=\"O1\"\n )\n\n return model\n\n def _wrap_model(self, model, training=True, dataloader=None):\n if self.args.use_ipex:\n dtype = torch.bfloat16 if self.use_cpu_amp else torch.float32\n model = self.ipex_optimize_model(model, training, dtype=dtype)\n\n if is_sagemaker_mp_enabled():\n # Wrapping the base model twice in a DistributedModel will raise an error.\n if isinstance(self.model_wrapped, smp.model.DistributedModel):\n return self.model_wrapped\n return smp.DistributedModel(model, backward_passes_per_step=self.args.gradient_accumulation_steps)\n\n # already initialized its own DDP and AMP\n if self.deepspeed:\n return self.deepspeed\n\n # train/eval could be run multiple-times - if already wrapped, don't re-wrap it again\n if unwrap_model(model) is not model:\n return model\n\n # Mixed precision training with apex (torch < 1.6)\n if self.use_apex and training:\n model, self.optimizer = amp.initialize(model, self.optimizer, opt_level=self.args.fp16_opt_level)\n\n # Multi-gpu training (should be after apex fp16 initialization) / 8bit models does not support DDP\n if self.args.n_gpu > 1 and not getattr(model, \"is_loaded_in_8bit\", False):\n model = nn.DataParallel(model)\n\n if self.args.jit_mode_eval:\n start_time = time.time()\n model = self.torch_jit_model_eval(model, dataloader, training)\n self.jit_compilation_time = round(time.time() - start_time, 4)\n\n # Note: in torch.distributed mode, there's no point in wrapping the model\n # inside a DistributedDataParallel as we'll be under `no_grad` anyways.\n if not training:\n return model\n\n # Distributed training (should be after apex fp16 initialization)\n if self.sharded_ddp is not None:\n # Sharded DDP!\n if self.sharded_ddp == ShardedDDPOption.SIMPLE:\n model = ShardedDDP(model, self.optimizer)\n else:\n mixed_precision = self.args.fp16 or self.args.bf16\n cpu_offload = ShardedDDPOption.OFFLOAD in self.args.sharded_ddp\n zero_3 = self.sharded_ddp == ShardedDDPOption.ZERO_DP_3\n # XXX: Breaking the self.model convention but I see no way around it for now.\n if ShardedDDPOption.AUTO_WRAP in self.args.sharded_ddp:\n model = auto_wrap(model)\n self.model = model = FullyShardedDDP(\n model,\n mixed_precision=mixed_precision,\n reshard_after_forward=zero_3,\n cpu_offload=cpu_offload,\n ).to(self.args.device)\n # Distributed training using PyTorch FSDP\n elif self.fsdp is not None:\n if not self.args.fsdp_config[\"xla\"]:\n # PyTorch FSDP!\n from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, MixedPrecision\n from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP\n from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy, transformer_auto_wrap_policy\n\n if FSDPOption.OFFLOAD in self.args.fsdp:\n cpu_offload = CPUOffload(offload_params=True)\n else:\n cpu_offload = CPUOffload(offload_params=False)\n\n auto_wrap_policy = None\n\n if FSDPOption.AUTO_WRAP in self.args.fsdp:\n if self.args.fsdp_config[\"fsdp_min_num_params\"] > 0:\n auto_wrap_policy = functools.partial(\n size_based_auto_wrap_policy, min_num_params=self.args.fsdp_config[\"fsdp_min_num_params\"]\n )\n elif self.args.fsdp_config.get(\"fsdp_transformer_layer_cls_to_wrap\", None) is not None:\n transformer_cls_to_wrap = set()\n for layer_class in self.args.fsdp_config[\"fsdp_transformer_layer_cls_to_wrap\"]:\n transformer_cls = get_module_class_from_name(model, layer_class)\n if transformer_cls is None:\n raise Exception(\"Could not find the transformer layer class to wrap in the model.\")\n else:\n transformer_cls_to_wrap.add(transformer_cls)\n auto_wrap_policy = functools.partial(\n transformer_auto_wrap_policy,\n # Transformer layer class to wrap\n transformer_layer_cls=transformer_cls_to_wrap,\n )\n mixed_precision_policy = None\n dtype = None\n if self.args.fp16:\n dtype = torch.float16\n elif self.args.bf16:\n dtype = torch.bfloat16\n if dtype is not None:\n mixed_precision_policy = MixedPrecision(param_dtype=dtype, reduce_dtype=dtype, buffer_dtype=dtype)\n if type(model) != FSDP:\n # XXX: Breaking the self.model convention but I see no way around it for now.\n signature = inspect.signature(FSDP.__init__).parameters.keys()\n kwargs = {}\n for arg in [\"limit_all_gathers\", \"forward_prefetch\", \"backward_prefetch\"]:\n if arg in signature:\n kwargs[arg] = getattr(self, arg)\n self.model = model = FSDP(\n model,\n sharding_strategy=self.fsdp,\n cpu_offload=cpu_offload,\n auto_wrap_policy=auto_wrap_policy,\n mixed_precision=mixed_precision_policy,\n device_id=self.args.device,\n **kwargs,\n )\n else:\n try:\n from torch_xla.distributed.fsdp import XlaFullyShardedDataParallel as FSDP\n from torch_xla.distributed.fsdp import checkpoint_module\n from torch_xla.distributed.fsdp.wrap import (\n size_based_auto_wrap_policy,\n transformer_auto_wrap_policy,\n )\n except ImportError:\n raise ImportError(\"Missing XLA FSDP related module; please make sure to use torch-xla >= 2.0.\")\n auto_wrap_policy = None\n auto_wrapper_callable = None\n if self.args.fsdp_config[\"fsdp_min_num_params\"] > 0:\n auto_wrap_policy = functools.partial(\n size_based_auto_wrap_policy, min_num_params=self.args.fsdp_config[\"fsdp_min_num_params\"]\n )\n elif self.args.fsdp_config.get(\"fsdp_transformer_layer_cls_to_wrap\", None) is not None:\n transformer_cls_to_wrap = set()\n for layer_class in self.args.fsdp_config[\"fsdp_transformer_layer_cls_to_wrap\"]:\n transformer_cls = get_module_class_from_name(model, layer_class)\n if transformer_cls is None:\n raise Exception(\"Could not find the transformer layer class to wrap in the model.\")\n else:\n transformer_cls_to_wrap.add(transformer_cls)\n auto_wrap_policy = functools.partial(\n transformer_auto_wrap_policy,\n # Transformer layer class to wrap\n transformer_layer_cls=transformer_cls_to_wrap,\n )\n fsdp_kwargs = self.args.xla_fsdp_config\n if self.args.fsdp_config[\"xla_fsdp_grad_ckpt\"]:\n # Apply gradient checkpointing to auto-wrapped sub-modules if specified\n def auto_wrapper_callable(m, *args, **kwargs):\n return FSDP(checkpoint_module(m), *args, **kwargs)\n\n # Wrap the base model with an outer FSDP wrapper\n self.model = model = FSDP(\n model,\n auto_wrap_policy=auto_wrap_policy,\n auto_wrapper_callable=auto_wrapper_callable,\n **fsdp_kwargs,\n )\n\n # Patch `xm.optimizer_step` should not reduce gradients in this case,\n # as FSDP does not need gradient reduction over sharded parameters.\n def patched_optimizer_step(optimizer, barrier=False, optimizer_args={}):\n loss = optimizer.step(**optimizer_args)\n if barrier:\n xm.mark_step()\n return loss\n\n xm.optimizer_step = patched_optimizer_step\n elif is_sagemaker_dp_enabled():\n model = nn.parallel.DistributedDataParallel(\n model, device_ids=[int(os.getenv(\"SMDATAPARALLEL_LOCAL_RANK\"))]\n )\n elif self.args.local_rank != -1:\n kwargs = {}\n if self.args.ddp_find_unused_parameters is not None:\n kwargs[\"find_unused_parameters\"] = self.args.ddp_find_unused_parameters\n elif isinstance(model, PreTrainedModel):\n # find_unused_parameters breaks checkpointing as per\n # https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021\n kwargs[\"find_unused_parameters\"] = not model.is_gradient_checkpointing\n else:\n kwargs[\"find_unused_parameters\"] = True\n\n if self.args.ddp_bucket_cap_mb is not None:\n kwargs[\"bucket_cap_mb\"] = self.args.ddp_bucket_cap_mb\n if is_torch_neuroncore_available():\n return model\n model = nn.parallel.DistributedDataParallel(\n model,\n device_ids=[self.args.local_rank] if self.args._n_gpu != 0 else None,\n output_device=self.args.local_rank if self.args._n_gpu != 0 else None,\n **kwargs,\n )\n\n # torch.compile() needs to be called after wrapping the model with FSDP or DDP\n # to ensure that it accounts for the graph breaks required by those wrappers\n if self.args.torch_compile:\n model = torch.compile(model, backend=self.args.torch_compile_backend, mode=self.args.torch_compile_mode)\n\n return model\n\n def train(\n self,\n resume_from_checkpoint: Optional[Union[str, bool]] = None,\n trial: Union[\"optuna.Trial\", Dict[str, Any]] = None,\n ignore_keys_for_eval: Optional[List[str]] = None,\n **kwargs,\n ):\n \"\"\"\n Main training entry point.\n\n Args:\n resume_from_checkpoint (`str` or `bool`, *optional*):\n If a `str`, local path to a saved checkpoint as saved by a previous instance of [`Trainer`]. If a\n `bool` and equals `True`, load the last checkpoint in *args.output_dir* as saved by a previous instance\n of [`Trainer`]. If present, training will resume from the model/optimizer/scheduler states loaded here.\n trial (`optuna.Trial` or `Dict[str, Any]`, *optional*):\n The trial run or the hyperparameter dictionary for hyperparameter search.\n ignore_keys_for_eval (`List[str]`, *optional*)\n A list of keys in the output of your model (if it is a dictionary) that should be ignored when\n gathering predictions for evaluation during the training.\n kwargs:\n Additional keyword arguments used to hide deprecated arguments\n \"\"\"\n if resume_from_checkpoint is False:\n resume_from_checkpoint = None\n\n # memory metrics - must set up as early as possible\n self._memory_tracker.start()\n\n args = self.args\n\n self.is_in_train = True\n\n # do_train is not a reliable argument, as it might not be set and .train() still called, so\n # the following is a workaround:\n if (args.fp16_full_eval or args.bf16_full_eval) and not args.do_train:\n self._move_model_to_device(self.model, args.device)\n\n if \"model_path\" in kwargs:\n resume_from_checkpoint = kwargs.pop(\"model_path\")\n warnings.warn(\n \"`model_path` is deprecated and will be removed in a future version. Use `resume_from_checkpoint` \"\n \"instead.\",\n FutureWarning,\n )\n if len(kwargs) > 0:\n raise TypeError(f\"train() received got unexpected keyword arguments: {', '.join(list(kwargs.keys()))}.\")\n # This might change the seed so needs to run first.\n self._hp_search_setup(trial)\n self._train_batch_size = self.args.train_batch_size\n\n # Model re-init\n model_reloaded = False\n if self.model_init is not None:\n # Seed must be set before instantiating the model when using model_init.\n enable_full_determinism(self.args.seed) if self.args.full_determinism else set_seed(self.args.seed)\n self.model = self.call_model_init(trial)\n model_reloaded = True\n # Reinitializes optimizer and scheduler\n self.optimizer, self.lr_scheduler = None, None\n\n # Load potential model checkpoint\n if isinstance(resume_from_checkpoint, bool) and resume_from_checkpoint:\n resume_from_checkpoint = get_last_checkpoint(args.output_dir)\n if resume_from_checkpoint is None:\n raise ValueError(f\"No valid checkpoint found in output directory ({args.output_dir})\")\n\n if resume_from_checkpoint is not None and not is_sagemaker_mp_enabled() and args.deepspeed is None:\n self._load_from_checkpoint(resume_from_checkpoint)\n\n # If model was re-initialized, put it on the right device and update self.model_wrapped\n if model_reloaded:\n if self.place_model_on_device:\n self._move_model_to_device(self.model, args.device)\n self.model_wrapped = self.model\n\n inner_training_loop = find_executable_batch_size(\n self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size\n )\n return inner_training_loop(\n args=args,\n resume_from_checkpoint=resume_from_checkpoint,\n trial=trial,\n ignore_keys_for_eval=ignore_keys_for_eval,\n )\n\n def _inner_training_loop(\n self, batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None\n ):\n self._train_batch_size = batch_size\n # Data loader and number of training steps\n train_dataloader = self.get_train_dataloader()\n\n # Setting up training control variables:\n # number of training epochs: num_train_epochs\n # number of training steps per epoch: num_update_steps_per_epoch\n # total number of training steps to execute: max_steps\n total_train_batch_size = args.train_batch_size * args.gradient_accumulation_steps * args.world_size\n\n len_dataloader = None\n if has_length(train_dataloader):\n len_dataloader = len(train_dataloader)\n num_update_steps_per_epoch = len_dataloader // args.gradient_accumulation_steps\n num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)\n num_examples = self.num_examples(train_dataloader)\n if args.max_steps > 0:\n max_steps = args.max_steps\n num_train_epochs = args.max_steps // num_update_steps_per_epoch + int(\n args.max_steps % num_update_steps_per_epoch > 0\n )\n # May be slightly incorrect if the last batch in the training dataloader has a smaller size but it's\n # the best we can do.\n num_train_samples = args.max_steps * total_train_batch_size\n else:\n max_steps = math.ceil(args.num_train_epochs * num_update_steps_per_epoch)\n num_train_epochs = math.ceil(args.num_train_epochs)\n num_train_samples = self.num_examples(train_dataloader) * args.num_train_epochs\n elif args.max_steps > 0: # Rely on max_steps when dataloader does not have a working size\n max_steps = args.max_steps\n # Setting a very large number of epochs so we go as many times as necessary over the iterator.\n num_train_epochs = sys.maxsize\n num_update_steps_per_epoch = max_steps\n num_examples = total_train_batch_size * args.max_steps\n num_train_samples = args.max_steps * total_train_batch_size\n else:\n raise ValueError(\n \"args.max_steps must be set to a positive value if dataloader does not have a length, was\"\n f\" {args.max_steps}\"\n )\n\n if DebugOption.UNDERFLOW_OVERFLOW in self.args.debug:\n if self.args.n_gpu > 1:\n # nn.DataParallel(model) replicates the model, creating new variables and module\n # references registered here no longer work on other gpus, breaking the module\n raise ValueError(\n \"Currently --debug underflow_overflow is not supported under DP. Please use DDP\"\n \" (torch.distributed.launch).\"\n )\n else:\n debug_overflow = DebugUnderflowOverflow(self.model) # noqa\n\n delay_optimizer_creation = (\n self.sharded_ddp is not None\n and self.sharded_ddp != ShardedDDPOption.SIMPLE\n or is_sagemaker_mp_enabled()\n or self.fsdp is not None\n )\n if args.deepspeed:\n deepspeed_engine, optimizer, lr_scheduler = deepspeed_init(\n self, num_training_steps=max_steps, resume_from_checkpoint=resume_from_checkpoint\n )\n self.model = deepspeed_engine.module\n self.model_wrapped = deepspeed_engine\n self.deepspeed = deepspeed_engine\n self.optimizer = optimizer\n self.lr_scheduler = lr_scheduler\n elif not delay_optimizer_creation:\n self.create_optimizer_and_scheduler(num_training_steps=max_steps)\n\n self.state = TrainerState()\n self.state.is_hyper_param_search = trial is not None\n\n # Activate gradient checkpointing if needed\n if args.gradient_checkpointing:\n self.model.gradient_checkpointing_enable()\n\n model = self._wrap_model(self.model_wrapped)\n\n if is_sagemaker_mp_enabled() and resume_from_checkpoint is not None:\n self._load_from_checkpoint(resume_from_checkpoint, model)\n\n # for the rest of this function `model` is the outside model, whether it was wrapped or not\n if model is not self.model:\n self.model_wrapped = model\n\n if delay_optimizer_creation:\n self.create_optimizer_and_scheduler(num_training_steps=max_steps)\n\n # Check if saved optimizer or scheduler states exist\n self._load_optimizer_and_scheduler(resume_from_checkpoint)\n\n # important: at this point:\n # self.model is the Transformers Model\n # self.model_wrapped is DDP(Transformers Model), Deepspeed(Transformers Model), etc.\n\n # Train!\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {num_examples:,}\")\n logger.info(f\" Num Epochs = {num_train_epochs:,}\")\n logger.info(f\" Instantaneous batch size per device = {args.per_device_train_batch_size:,}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size:,}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {max_steps:,}\")\n logger.info(f\" Number of trainable parameters = {get_model_param_count(model, trainable_only=True):,}\")\n\n self.state.epoch = 0\n start_time = time.time()\n epochs_trained = 0\n steps_trained_in_current_epoch = 0\n steps_trained_progress_bar = None\n\n # Check if continuing training from a checkpoint\n if resume_from_checkpoint is not None and os.path.isfile(\n os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME)\n ):\n self.state = TrainerState.load_from_json(os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME))\n epochs_trained = self.state.global_step // num_update_steps_per_epoch\n if not args.ignore_data_skip:\n steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)\n steps_trained_in_current_epoch *= args.gradient_accumulation_steps\n else:\n steps_trained_in_current_epoch = 0\n\n logger.info(\" Continuing training from checkpoint, will skip to saved global_step\")\n logger.info(f\" Continuing training from epoch {epochs_trained}\")\n logger.info(f\" Continuing training from global step {self.state.global_step}\")\n if not args.ignore_data_skip:\n if skip_first_batches is None:\n logger.info(\n f\" Will skip the first {epochs_trained} epochs then the first\"\n f\" {steps_trained_in_current_epoch} batches in the first epoch. If this takes a lot of time,\"\n \" you can install the latest version of Accelerate with `pip install -U accelerate`.You can\"\n \" also add the `--ignore_data_skip` flag to your launch command, but you will resume the\"\n \" training on data already seen by your model.\"\n )\n else:\n logger.info(\n f\" Will skip the first {epochs_trained} epochs then the first\"\n f\" {steps_trained_in_current_epoch} batches in the first epoch.\"\n )\n if self.is_local_process_zero() and not args.disable_tqdm and skip_first_batches is None:\n steps_trained_progress_bar = tqdm(total=steps_trained_in_current_epoch)\n steps_trained_progress_bar.set_description(\"Skipping the first batches\")\n\n # Update the references\n self.callback_handler.model = self.model\n self.callback_handler.optimizer = self.optimizer\n self.callback_handler.lr_scheduler = self.lr_scheduler\n self.callback_handler.train_dataloader = train_dataloader\n if self.hp_name is not None and self._trial is not None:\n # use self._trial because the SigOpt/Optuna hpo only call `_hp_search_setup(trial)` instead of passing trial\n # parameter to Train when using DDP.\n self.state.trial_name = self.hp_name(self._trial)\n if trial is not None:\n assignments = trial.assignments if self.hp_search_backend == HPSearchBackend.SIGOPT else trial\n self.state.trial_params = hp_params(assignments)\n else:\n self.state.trial_params = None\n # This should be the same if the state has been saved but in case the training arguments changed, it's safer\n # to set this after the load.\n self.state.max_steps = max_steps\n self.state.num_train_epochs = num_train_epochs\n self.state.is_local_process_zero = self.is_local_process_zero()\n self.state.is_world_process_zero = self.is_world_process_zero()\n\n # tr_loss is a tensor to avoid synchronization of TPUs through .item()\n tr_loss = torch.tensor(0.0).to(args.device)\n # _total_loss_scalar is updated everytime .item() has to be called on tr_loss and stores the sum of all losses\n self._total_loss_scalar = 0.0\n self._globalstep_last_logged = self.state.global_step\n model.zero_grad()\n\n self.control = self.callback_handler.on_train_begin(args, self.state, self.control)\n\n # Skip the first epochs_trained epochs to get the random state of the dataloader at the right point.\n if not args.ignore_data_skip:\n for epoch in range(epochs_trained):\n is_random_sampler = hasattr(train_dataloader, \"sampler\") and isinstance(\n train_dataloader.sampler, RandomSampler\n )\n if is_torch_less_than_1_11 or not is_random_sampler:\n # We just need to begin an iteration to create the randomization of the sampler.\n # That was before PyTorch 1.11 however...\n for _ in train_dataloader:\n break\n else:\n # Otherwise we need to call the whooooole sampler cause there is some random operation added\n # AT THE VERY END!\n _ = list(train_dataloader.sampler)\n\n total_batched_samples = 0\n for epoch in range(epochs_trained, num_train_epochs):\n ### 吴超逸加 ###\n if isinstance(train_dataloader, DataLoader) and (isinstance(train_dataloader.sampler, DistributedSampler) or self.args.data_sampler != None):\n train_dataloader.sampler.set_epoch(epoch)\n elif hasattr(train_dataloader, \"dataset\") and (isinstance(train_dataloader.sampler, DistributedSampler) or self.args.data_sampler != None):\n train_dataloader.dataset.set_epoch(epoch)\n\n if is_torch_tpu_available():\n parallel_loader = pl.ParallelLoader(train_dataloader, [args.device]).per_device_loader(args.device)\n epoch_iterator = parallel_loader\n else:\n epoch_iterator = train_dataloader\n\n # Reset the past mems state at the beginning of each epoch if necessary.\n if args.past_index >= 0:\n self._past = None\n\n steps_in_epoch = (\n len(epoch_iterator)\n if len_dataloader is not None\n else args.max_steps * args.gradient_accumulation_steps\n )\n self.control = self.callback_handler.on_epoch_begin(args, self.state, self.control)\n\n if epoch == epochs_trained and resume_from_checkpoint is not None and steps_trained_in_current_epoch == 0:\n self._load_rng_state(resume_from_checkpoint)\n\n rng_to_sync = False\n steps_skipped = 0\n if skip_first_batches is not None and steps_trained_in_current_epoch > 0:\n epoch_iterator = skip_first_batches(epoch_iterator, steps_trained_in_current_epoch)\n steps_skipped = steps_trained_in_current_epoch\n steps_trained_in_current_epoch = 0\n rng_to_sync = True\n\n step = -1\n for step, inputs in enumerate(epoch_iterator):\n total_batched_samples += 1\n if rng_to_sync:\n self._load_rng_state(resume_from_checkpoint)\n rng_to_sync = False\n\n # Skip past any already trained steps if resuming training\n if steps_trained_in_current_epoch > 0:\n steps_trained_in_current_epoch -= 1\n if steps_trained_progress_bar is not None:\n steps_trained_progress_bar.update(1)\n if steps_trained_in_current_epoch == 0:\n self._load_rng_state(resume_from_checkpoint)\n continue\n elif steps_trained_progress_bar is not None:\n steps_trained_progress_bar.close()\n steps_trained_progress_bar = None\n\n if step % args.gradient_accumulation_steps == 0:\n self.control = self.callback_handler.on_step_begin(args, self.state, self.control)\n\n if (\n (total_batched_samples % args.gradient_accumulation_steps != 0)\n and args.local_rank != -1\n and args._no_sync_in_gradient_accumulation\n ):\n # Avoid unnecessary DDP synchronization since there will be no backward pass on this example.\n with model.no_sync():\n tr_loss_step = self.training_step(model, inputs)\n else:\n tr_loss_step = self.training_step(model, inputs)\n\n if (\n args.logging_nan_inf_filter\n and not is_torch_tpu_available()\n and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))\n ):\n # if loss is nan or inf simply add the average of previous logged losses\n tr_loss += tr_loss / (1 + self.state.global_step - self._globalstep_last_logged)\n else:\n tr_loss += tr_loss_step\n\n self.current_flos += float(self.floating_point_ops(inputs))\n\n # Optimizer step for deepspeed must be called on every step regardless of the value of gradient_accumulation_steps\n if self.deepspeed:\n self.deepspeed.step()\n\n if total_batched_samples % args.gradient_accumulation_steps == 0 or (\n # last step in epoch but step is always smaller than gradient_accumulation_steps\n steps_in_epoch <= args.gradient_accumulation_steps\n and (step + 1) == steps_in_epoch\n ):\n # Gradient clipping\n if args.max_grad_norm is not None and args.max_grad_norm > 0 and not self.deepspeed:\n # deepspeed does its own clipping\n\n if self.do_grad_scaling:\n # Reduce gradients first for XLA\n if is_torch_tpu_available():\n gradients = xm._fetch_gradients(self.optimizer)\n xm.all_reduce(\"sum\", gradients, scale=1.0 / xm.xrt_world_size())\n # AMP: gradients need unscaling\n self.scaler.unscale_(self.optimizer)\n\n if is_sagemaker_mp_enabled() and args.fp16:\n self.optimizer.clip_master_grads(args.max_grad_norm)\n elif hasattr(self.optimizer, \"clip_grad_norm\"):\n # Some optimizers (like the sharded optimizer) have a specific way to do gradient clipping\n self.optimizer.clip_grad_norm(args.max_grad_norm)\n elif hasattr(model, \"clip_grad_norm_\"):\n # Some models (like FullyShardedDDP) have a specific way to do gradient clipping\n model.clip_grad_norm_(args.max_grad_norm)\n else:\n # Revert to normal clipping otherwise, handling Apex or full precision\n nn.utils.clip_grad_norm_(\n amp.master_params(self.optimizer) if self.use_apex else model.parameters(),\n args.max_grad_norm,\n )\n\n # Optimizer step\n optimizer_was_run = True\n if self.deepspeed:\n pass # called outside the loop\n elif is_torch_tpu_available():\n if self.do_grad_scaling:\n self.scaler.step(self.optimizer)\n self.scaler.update()\n else:\n xm.optimizer_step(self.optimizer)\n elif self.do_grad_scaling:\n scale_before = self.scaler.get_scale()\n self.scaler.step(self.optimizer)\n self.scaler.update()\n scale_after = self.scaler.get_scale()\n optimizer_was_run = scale_before <= scale_after\n else:\n self.optimizer.step()\n\n if optimizer_was_run and not self.deepspeed:\n self.lr_scheduler.step()\n\n model.zero_grad()\n self.state.global_step += 1\n self.state.epoch = epoch + (step + 1 + steps_skipped) / steps_in_epoch\n self.control = self.callback_handler.on_step_end(args, self.state, self.control)\n\n self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)\n else:\n self.control = self.callback_handler.on_substep_end(args, self.state, self.control)\n\n if self.control.should_epoch_stop or self.control.should_training_stop:\n break\n if step < 0:\n logger.warning(\n \"There seems to be not a single sample in your epoch_iterator, stopping training at step\"\n f\" {self.state.global_step}! This is expected if you're using an IterableDataset and set\"\n f\" num_steps ({max_steps}) higher than the number of available samples.\"\n )\n self.control.should_training_stop = True\n\n self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)\n self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)\n\n if DebugOption.TPU_METRICS_DEBUG in self.args.debug:\n if is_torch_tpu_available():\n # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)\n xm.master_print(met.metrics_report())\n else:\n logger.warning(\n \"You enabled PyTorch/XLA debug metrics but you don't have a TPU \"\n \"configured. Check your training configuration if this is unexpected.\"\n )\n if self.control.should_training_stop:\n break\n\n if args.past_index and hasattr(self, \"_past\"):\n # Clean the state at the end of training\n delattr(self, \"_past\")\n\n logger.info(\"\\n\\nTraining completed. Do not forget to share your model on huggingface.co/models =)\\n\\n\")\n if args.load_best_model_at_end and self.state.best_model_checkpoint is not None:\n # Wait for everyone to get here so we are sur the model has been saved by process 0.\n if is_torch_tpu_available():\n xm.rendezvous(\"load_best_model_at_end\")\n elif args.local_rank != -1:\n dist.barrier()\n elif is_sagemaker_mp_enabled():\n smp.barrier()\n\n self._load_best_model()\n\n # add remaining tr_loss\n self._total_loss_scalar += tr_loss.item()\n train_loss = self._total_loss_scalar / self.state.global_step\n\n metrics = speed_metrics(\"train\", start_time, num_samples=num_train_samples, num_steps=self.state.max_steps)\n self.store_flos()\n metrics[\"total_flos\"] = self.state.total_flos\n metrics[\"train_loss\"] = train_loss\n\n self.is_in_train = False\n\n self._memory_tracker.stop_and_update_metrics(metrics)\n\n self.log(metrics)\n\n run_dir = self._get_output_dir(trial)\n checkpoints_sorted = self._sorted_checkpoints(use_mtime=False, output_dir=run_dir)\n\n # Delete the last checkpoint when save_total_limit=1 if it's different from the best checkpoint and process allowed to save.\n if self.args.should_save and self.state.best_model_checkpoint is not None and self.args.save_total_limit == 1:\n for checkpoint in checkpoints_sorted:\n if checkpoint != self.state.best_model_checkpoint:\n logger.info(f\"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit\")\n shutil.rmtree(checkpoint)\n\n self.control = self.callback_handler.on_train_end(args, self.state, self.control)\n\n return TrainOutput(self.state.global_step, train_loss, metrics)\n\n def _get_output_dir(self, trial):\n if self.hp_search_backend is not None and trial is not None:\n if self.hp_search_backend == HPSearchBackend.OPTUNA:\n run_id = trial.number\n elif self.hp_search_backend == HPSearchBackend.RAY:\n from ray import tune\n\n run_id = tune.get_trial_id()\n elif self.hp_search_backend == HPSearchBackend.SIGOPT:\n run_id = trial.id\n elif self.hp_search_backend == HPSearchBackend.WANDB:\n import wandb\n\n run_id = wandb.run.id\n run_name = self.hp_name(trial) if self.hp_name is not None else f\"run-{run_id}\"\n run_dir = os.path.join(self.args.output_dir, run_name)\n else:\n run_dir = self.args.output_dir\n return run_dir\n\n def _load_from_checkpoint(self, resume_from_checkpoint, model=None):\n if model is None:\n model = self.model\n\n config_file = os.path.join(resume_from_checkpoint, CONFIG_NAME)\n\n weights_file = os.path.join(resume_from_checkpoint, WEIGHTS_NAME)\n weights_index_file = os.path.join(resume_from_checkpoint, WEIGHTS_INDEX_NAME)\n safe_weights_file = os.path.join(resume_from_checkpoint, SAFE_WEIGHTS_NAME)\n safe_weights_index_file = os.path.join(resume_from_checkpoint, SAFE_WEIGHTS_INDEX_NAME)\n\n if not any(\n [os.path.isfile(f) for f in [weights_file, safe_weights_file, weights_index_file, safe_weights_index_file]]\n ):\n raise ValueError(f\"Can't find a valid checkpoint at {resume_from_checkpoint}\")\n\n logger.info(f\"Loading model from {resume_from_checkpoint}.\")\n\n if os.path.isfile(config_file):\n config = PretrainedConfig.from_json_file(config_file)\n checkpoint_version = config.transformers_version\n if checkpoint_version is not None and checkpoint_version != __version__:\n logger.warning(\n f\"You are resuming training from a checkpoint trained with {checkpoint_version} of \"\n f\"Transformers but your current version is {__version__}. This is not recommended and could \"\n \"yield to errors or unwanted behaviors.\"\n )\n\n if os.path.isfile(weights_file) or os.path.isfile(safe_weights_file):\n # If the model is on the GPU, it still works!\n if is_sagemaker_mp_enabled():\n if os.path.isfile(os.path.join(resume_from_checkpoint, \"user_content.pt\")):\n # If the 'user_content.pt' file exists, load with the new smp api.\n # Checkpoint must have been saved with the new smp api.\n smp.resume_from_checkpoint(\n path=resume_from_checkpoint, tag=WEIGHTS_NAME, partial=False, load_optimizer=False\n )\n else:\n # If the 'user_content.pt' file does NOT exist, load with the old smp api.\n # Checkpoint must have been saved with the old smp api.\n if hasattr(self.args, \"fp16\") and self.args.fp16 is True:\n logger.warning(\n \"Enabling FP16 and loading from smp < 1.10 checkpoint together is not suppported.\"\n )\n state_dict = torch.load(weights_file, map_location=\"cpu\")\n # Required for smp to not auto-translate state_dict from hf to smp (is already smp).\n state_dict[\"_smp_is_partial\"] = False\n load_result = model.load_state_dict(state_dict, strict=True)\n # release memory\n del state_dict\n else:\n # We load the model state dict on the CPU to avoid an OOM error.\n if self.args.save_safetensors and os.path.isfile(safe_weights_file):\n state_dict = safetensors.torch.load_file(safe_weights_file, device=\"cpu\")\n else:\n state_dict = torch.load(weights_file, map_location=\"cpu\")\n\n # workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963\n # which takes *args instead of **kwargs\n load_result = model.load_state_dict(state_dict, False)\n # release memory\n del state_dict\n self._issue_warnings_after_load(load_result)\n else:\n # We load the sharded checkpoint\n load_result = load_sharded_checkpoint(\n model, resume_from_checkpoint, strict=is_sagemaker_mp_enabled(), prefer_safe=self.args.save_safetensors\n )\n if not is_sagemaker_mp_enabled():\n self._issue_warnings_after_load(load_result)\n\n def _load_best_model(self):\n logger.info(f\"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric}).\")\n best_model_path = os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME)\n best_safe_model_path = os.path.join(self.state.best_model_checkpoint, SAFE_WEIGHTS_NAME)\n model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model\n if os.path.exists(best_model_path) or os.path.exists(best_safe_model_path):\n if self.deepspeed:\n if self.model_wrapped is not None:\n # this removes the pre-hooks from the previous engine\n self.model_wrapped.destroy()\n self.model_wrapped = None\n\n # temp hack until Deepspeed fixes the problem with resume from an existing engine that did some stepping\n deepspeed_engine, optimizer, lr_scheduler = deepspeed_init(\n self,\n num_training_steps=self.args.max_steps,\n resume_from_checkpoint=self.state.best_model_checkpoint,\n )\n self.model = deepspeed_engine.module\n self.model_wrapped = deepspeed_engine\n self.deepspeed = deepspeed_engine\n self.optimizer = optimizer\n self.lr_scheduler = lr_scheduler\n else:\n if is_sagemaker_mp_enabled():\n if os.path.isfile(os.path.join(self.state.best_model_checkpoint, \"user_content.pt\")):\n # If the 'user_content.pt' file exists, load with the new smp api.\n # Checkpoint must have been saved with the new smp api.\n smp.resume_from_checkpoint(\n path=self.state.best_model_checkpoint,\n tag=WEIGHTS_NAME,\n partial=False,\n load_optimizer=False,\n )\n else:\n # If the 'user_content.pt' file does NOT exist, load with the old smp api.\n # Checkpoint must have been saved with the old smp api.\n if self.args.save_safetensors and os.path.isfile(best_safe_model_path):\n state_dict = safetensors.torch.load_file(best_safe_model_path, device=\"cpu\")\n else:\n state_dict = torch.load(best_model_path, map_location=\"cpu\")\n\n state_dict[\"_smp_is_partial\"] = False\n load_result = model.load_state_dict(state_dict, strict=True)\n else:\n # We load the model state dict on the CPU to avoid an OOM error.\n if self.args.save_safetensors and os.path.isfile(best_safe_model_path):\n state_dict = safetensors.torch.load_file(best_safe_model_path, device=\"cpu\")\n else:\n state_dict = torch.load(best_model_path, map_location=\"cpu\")\n\n # If the model is on the GPU, it still works!\n # workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963\n # which takes *args instead of **kwargs\n load_result = model.load_state_dict(state_dict, False)\n if not is_sagemaker_mp_enabled():\n self._issue_warnings_after_load(load_result)\n elif os.path.exists(os.path.join(self.state.best_model_checkpoint, WEIGHTS_INDEX_NAME)):\n load_result = load_sharded_checkpoint(\n model, self.state.best_model_checkpoint, strict=is_sagemaker_mp_enabled()\n )\n if not is_sagemaker_mp_enabled():\n self._issue_warnings_after_load(load_result)\n else:\n logger.warning(\n f\"Could not locate the best model at {best_model_path}, if you are running a distributed training \"\n \"on multiple nodes, you should activate `--save_on_each_node`.\"\n )\n\n def _issue_warnings_after_load(self, load_result):\n if len(load_result.missing_keys) != 0:\n if self.model._keys_to_ignore_on_save is not None and set(load_result.missing_keys) == set(\n self.model._keys_to_ignore_on_save\n ):\n self.model.tie_weights()\n else:\n logger.warning(f\"There were missing keys in the checkpoint model loaded: {load_result.missing_keys}.\")\n if len(load_result.unexpected_keys) != 0:\n logger.warning(\n f\"There were unexpected keys in the checkpoint model loaded: {load_result.unexpected_keys}.\"\n )\n\n def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignore_keys_for_eval):\n if self.control.should_log:\n if is_torch_tpu_available():\n xm.mark_step()\n\n logs: Dict[str, float] = {}\n\n # all_gather + mean() to get average loss over all processes\n tr_loss_scalar = self._nested_gather(tr_loss).mean().item()\n\n # reset tr_loss to zero\n tr_loss -= tr_loss\n\n logs[\"loss\"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)\n logs[\"learning_rate\"] = self._get_learning_rate()\n\n self._total_loss_scalar += tr_loss_scalar\n self._globalstep_last_logged = self.state.global_step\n self.store_flos()\n\n self.log(logs)\n\n metrics = None\n if self.control.should_evaluate:\n if isinstance(self.eval_dataset, dict):\n metrics = {}\n for eval_dataset_name, eval_dataset in self.eval_dataset.items():\n dataset_metrics = self.evaluate(\n eval_dataset=eval_dataset,\n ignore_keys=ignore_keys_for_eval,\n metric_key_prefix=f\"eval_{eval_dataset_name}\",\n )\n metrics.update(dataset_metrics)\n else:\n metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)\n self._report_to_hp_search(trial, self.state.global_step, metrics)\n\n if self.control.should_save:\n self._save_checkpoint(model, trial, metrics=metrics)\n self.control = self.callback_handler.on_save(self.args, self.state, self.control)\n\n def _load_rng_state(self, checkpoint):\n # Load RNG states from `checkpoint`\n if checkpoint is None:\n return\n\n if self.args.world_size > 1:\n process_index = self.args.process_index\n rng_file = os.path.join(checkpoint, f\"rng_state_{process_index}.pth\")\n if not os.path.isfile(rng_file):\n logger.info(\n f\"Didn't find an RNG file for process {process_index}, if you are resuming a training that \"\n \"wasn't launched in a distributed fashion, reproducibility is not guaranteed.\"\n )\n return\n else:\n rng_file = os.path.join(checkpoint, \"rng_state.pth\")\n if not os.path.isfile(rng_file):\n logger.info(\n \"Didn't find an RNG file, if you are resuming a training that was launched in a distributed \"\n \"fashion, reproducibility is not guaranteed.\"\n )\n return\n\n checkpoint_rng_state = torch.load(rng_file)\n random.setstate(checkpoint_rng_state[\"python\"])\n np.random.set_state(checkpoint_rng_state[\"numpy\"])\n torch.random.set_rng_state(checkpoint_rng_state[\"cpu\"])\n if torch.cuda.is_available():\n if self.args.local_rank != -1:\n torch.cuda.random.set_rng_state(checkpoint_rng_state[\"cuda\"])\n else:\n try:\n torch.cuda.random.set_rng_state_all(checkpoint_rng_state[\"cuda\"])\n except Exception as e:\n logger.info(\n f\"Didn't manage to set back the RNG states of the GPU because of the following error:\\n {e}\"\n \"\\nThis won't yield the same results as if the training had not been interrupted.\"\n )\n if is_torch_tpu_available():\n xm.set_rng_state(checkpoint_rng_state[\"xla\"])\n\n def _save_checkpoint(self, model, trial, metrics=None):\n # In all cases, including ddp/dp/deepspeed, self.model is always a reference to the model we\n # want to save except FullyShardedDDP.\n # assert unwrap_model(model) is self.model, \"internal model should be a reference to self.model\"\n\n # Save model checkpoint\n checkpoint_folder = f\"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}\"\n\n if self.hp_search_backend is None and trial is None:\n self.store_flos()\n\n run_dir = self._get_output_dir(trial=trial)\n output_dir = os.path.join(run_dir, checkpoint_folder)\n self.save_model(output_dir, _internal_call=True)\n if self.deepspeed:\n # under zero3 model file itself doesn't get saved since it's bogus! Unless deepspeed\n # config `stage3_gather_16bit_weights_on_model_save` is True\n self.deepspeed.save_checkpoint(output_dir)\n\n # Save optimizer and scheduler\n if self.sharded_ddp == ShardedDDPOption.SIMPLE:\n self.optimizer.consolidate_state_dict()\n\n if is_torch_tpu_available():\n xm.rendezvous(\"saving_optimizer_states\")\n xm.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))\n with warnings.catch_warnings(record=True) as caught_warnings:\n xm.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))\n reissue_pt_warnings(caught_warnings)\n elif is_sagemaker_mp_enabled():\n opt_state_dict = self.optimizer.local_state_dict(gather_if_shard=False)\n smp.barrier()\n if smp.rdp_rank() == 0 or smp.state.cfg.shard_optimizer_state:\n smp.save(\n opt_state_dict,\n os.path.join(output_dir, OPTIMIZER_NAME),\n partial=True,\n v3=smp.state.cfg.shard_optimizer_state,\n )\n if self.args.should_save:\n with warnings.catch_warnings(record=True) as caught_warnings:\n torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))\n reissue_pt_warnings(caught_warnings)\n if self.do_grad_scaling:\n torch.save(self.scaler.state_dict(), os.path.join(output_dir, SCALER_NAME))\n elif self.args.should_save and not self.deepspeed:\n # deepspeed.save_checkpoint above saves model/optim/sched\n torch.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))\n with warnings.catch_warnings(record=True) as caught_warnings:\n torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))\n reissue_pt_warnings(caught_warnings)\n if self.do_grad_scaling:\n torch.save(self.scaler.state_dict(), os.path.join(output_dir, SCALER_NAME))\n\n # Determine the new best metric / best model checkpoint\n if metrics is not None and self.args.metric_for_best_model is not None:\n metric_to_check = self.args.metric_for_best_model\n if not metric_to_check.startswith(\"eval_\"):\n metric_to_check = f\"eval_{metric_to_check}\"\n metric_value = metrics[metric_to_check]\n\n operator = np.greater if self.args.greater_is_better else np.less\n if (\n self.state.best_metric is None\n or self.state.best_model_checkpoint is None\n or operator(metric_value, self.state.best_metric)\n ):\n self.state.best_metric = metric_value\n self.state.best_model_checkpoint = output_dir\n\n # Save the Trainer state\n if self.args.should_save:\n self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))\n\n # Save RNG state in non-distributed training\n rng_states = {\n \"python\": random.getstate(),\n \"numpy\": np.random.get_state(),\n \"cpu\": torch.random.get_rng_state(),\n }\n if torch.cuda.is_available():\n if self.args.local_rank == -1:\n # In non distributed, we save the global CUDA RNG state (will take care of DataParallel)\n rng_states[\"cuda\"] = torch.cuda.random.get_rng_state_all()\n else:\n rng_states[\"cuda\"] = torch.cuda.random.get_rng_state()\n\n if is_torch_tpu_available():\n rng_states[\"xla\"] = xm.get_rng_state()\n\n # A process can arrive here before the process 0 has a chance to save the model, in which case output_dir may\n # not yet exist.\n os.makedirs(output_dir, exist_ok=True)\n\n if self.args.world_size <= 1:\n torch.save(rng_states, os.path.join(output_dir, \"rng_state.pth\"))\n else:\n torch.save(rng_states, os.path.join(output_dir, f\"rng_state_{self.args.process_index}.pth\"))\n\n if self.args.push_to_hub:\n self._push_from_checkpoint(output_dir)\n\n # Maybe delete some older checkpoints.\n if self.args.should_save:\n self._rotate_checkpoints(use_mtime=True, output_dir=run_dir)\n\n def _load_optimizer_and_scheduler(self, checkpoint):\n \"\"\"If optimizer and scheduler states exist, load them.\"\"\"\n if checkpoint is None:\n return\n\n if self.deepspeed:\n # deepspeed loads optimizer/lr_scheduler together with the model in deepspeed_init\n return\n\n checkpoint_file_exists = (\n glob.glob(os.path.join(checkpoint, OPTIMIZER_NAME) + \"_*\")\n if is_sagemaker_mp_enabled()\n else os.path.isfile(os.path.join(checkpoint, OPTIMIZER_NAME))\n )\n if checkpoint_file_exists and os.path.isfile(os.path.join(checkpoint, SCHEDULER_NAME)):\n # Load in optimizer and scheduler states\n if is_torch_tpu_available():\n # On TPU we have to take some extra precautions to properly load the states on the right device.\n optimizer_state = torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location=\"cpu\")\n with warnings.catch_warnings(record=True) as caught_warnings:\n lr_scheduler_state = torch.load(os.path.join(checkpoint, SCHEDULER_NAME), map_location=\"cpu\")\n reissue_pt_warnings(caught_warnings)\n\n xm.send_cpu_data_to_device(optimizer_state, self.args.device)\n xm.send_cpu_data_to_device(lr_scheduler_state, self.args.device)\n\n self.optimizer.load_state_dict(optimizer_state)\n self.lr_scheduler.load_state_dict(lr_scheduler_state)\n else:\n if is_sagemaker_mp_enabled():\n if os.path.isfile(os.path.join(checkpoint, \"user_content.pt\")):\n # Optimizer checkpoint was saved with smp >= 1.10\n def opt_load_hook(mod, opt):\n opt.load_state_dict(smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True))\n\n else:\n # Optimizer checkpoint was saved with smp < 1.10\n def opt_load_hook(mod, opt):\n if IS_SAGEMAKER_MP_POST_1_10:\n opt.load_state_dict(\n smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True, back_compat=True)\n )\n else:\n opt.load_state_dict(smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True))\n\n self.model_wrapped.register_post_step_hook(opt_load_hook)\n else:\n # We use the CPU when training on one GPU to avoid OOM for GPU RAM when training big models.\n # In distributed training however, we load directly on each GPU and risk the GPU OOM as it's more\n # likely to get OOM on CPU (since we load num_gpu times the optimizer state\n map_location = self.args.device if self.args.world_size > 1 else \"cpu\"\n self.optimizer.load_state_dict(\n torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location=map_location)\n )\n with warnings.catch_warnings(record=True) as caught_warnings:\n self.lr_scheduler.load_state_dict(torch.load(os.path.join(checkpoint, SCHEDULER_NAME)))\n reissue_pt_warnings(caught_warnings)\n if self.do_grad_scaling and os.path.isfile(os.path.join(checkpoint, SCALER_NAME)):\n self.scaler.load_state_dict(torch.load(os.path.join(checkpoint, SCALER_NAME)))\n\n def hyperparameter_search(\n self,\n hp_space: Optional[Callable[[\"optuna.Trial\"], Dict[str, float]]] = None,\n compute_objective: Optional[Callable[[Dict[str, float]], float]] = None,\n n_trials: int = 20,\n direction: str = \"minimize\",\n backend: Optional[Union[\"str\", HPSearchBackend]] = None,\n hp_name: Optional[Callable[[\"optuna.Trial\"], str]] = None,\n **kwargs,\n ) -> BestRun:\n \"\"\"\n Launch an hyperparameter search using `optuna` or `Ray Tune` or `SigOpt`. The optimized quantity is determined\n by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided,\n the sum of all metrics otherwise.\n\n \n\n To use this method, you need to have provided a `model_init` when initializing your [`Trainer`]: we need to\n reinitialize the model at each new run. This is incompatible with the `optimizers` argument, so you need to\n subclass [`Trainer`] and override the method [`~Trainer.create_optimizer_and_scheduler`] for custom\n optimizer/scheduler.\n\n \n\n Args:\n hp_space (`Callable[[\"optuna.Trial\"], Dict[str, float]]`, *optional*):\n A function that defines the hyperparameter search space. Will default to\n [`~trainer_utils.default_hp_space_optuna`] or [`~trainer_utils.default_hp_space_ray`] or\n [`~trainer_utils.default_hp_space_sigopt`] depending on your backend.\n compute_objective (`Callable[[Dict[str, float]], float]`, *optional*):\n A function computing the objective to minimize or maximize from the metrics returned by the `evaluate`\n method. Will default to [`~trainer_utils.default_compute_objective`].\n n_trials (`int`, *optional*, defaults to 100):\n The number of trial runs to test.\n direction (`str`, *optional*, defaults to `\"minimize\"`):\n Whether to optimize greater or lower objects. Can be `\"minimize\"` or `\"maximize\"`, you should pick\n `\"minimize\"` when optimizing the validation loss, `\"maximize\"` when optimizing one or several metrics.\n backend (`str` or [`~training_utils.HPSearchBackend`], *optional*):\n The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending\n on which one is installed. If all are installed, will default to optuna.\n hp_name (`Callable[[\"optuna.Trial\"], str]]`, *optional*):\n A function that defines the trial/run name. Will default to None.\n kwargs (`Dict[str, Any]`, *optional*):\n Additional keyword arguments passed along to `optuna.create_study` or `ray.tune.run`. For more\n information see:\n\n - the documentation of\n [optuna.create_study](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html)\n - the documentation of [tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run)\n - the documentation of [sigopt](https://app.sigopt.com/docs/endpoints/experiments/create)\n\n Returns:\n [`trainer_utils.BestRun`]: All the information about the best run. Experiment summary can be found in\n `run_summary` attribute for Ray backend.\n \"\"\"\n if backend is None:\n backend = default_hp_search_backend()\n if backend is None:\n raise RuntimeError(\n \"At least one of optuna or ray should be installed. \"\n \"To install optuna run `pip install optuna`. \"\n \"To install ray run `pip install ray[tune]`. \"\n \"To install sigopt run `pip install sigopt`.\"\n )\n backend = HPSearchBackend(backend)\n if backend == HPSearchBackend.OPTUNA and not is_optuna_available():\n raise RuntimeError(\"You picked the optuna backend, but it is not installed. Use `pip install optuna`.\")\n if backend == HPSearchBackend.RAY and not is_ray_tune_available():\n raise RuntimeError(\n \"You picked the Ray Tune backend, but it is not installed. Use `pip install 'ray[tune]'`.\"\n )\n if backend == HPSearchBackend.SIGOPT and not is_sigopt_available():\n raise RuntimeError(\"You picked the sigopt backend, but it is not installed. Use `pip install sigopt`.\")\n if backend == HPSearchBackend.WANDB and not is_wandb_available():\n raise RuntimeError(\"You picked the wandb backend, but it is not installed. Use `pip install wandb`.\")\n self.hp_search_backend = backend\n if self.model_init is None:\n raise RuntimeError(\n \"To use hyperparameter search, you need to pass your model through a model_init function.\"\n )\n\n self.hp_space = default_hp_space[backend] if hp_space is None else hp_space\n self.hp_name = hp_name\n self.compute_objective = default_compute_objective if compute_objective is None else compute_objective\n\n backend_dict = {\n HPSearchBackend.OPTUNA: run_hp_search_optuna,\n HPSearchBackend.RAY: run_hp_search_ray,\n HPSearchBackend.SIGOPT: run_hp_search_sigopt,\n HPSearchBackend.WANDB: run_hp_search_wandb,\n }\n best_run = backend_dict[backend](self, n_trials, direction, **kwargs)\n\n self.hp_search_backend = None\n return best_run\n\n def log(self, logs: Dict[str, float]) -> None:\n \"\"\"\n Log `logs` on the various objects watching training.\n\n Subclass and override this method to inject custom behavior.\n\n Args:\n logs (`Dict[str, float]`):\n The values to log.\n \"\"\"\n if self.state.epoch is not None:\n logs[\"epoch\"] = round(self.state.epoch, 2)\n\n output = {**logs, **{\"step\": self.state.global_step}}\n self.state.log_history.append(output)\n self.control = self.callback_handler.on_log(self.args, self.state, self.control, logs)\n\n def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torch.Tensor, Any]:\n \"\"\"\n Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.\n \"\"\"\n if isinstance(data, Mapping):\n return type(data)({k: self._prepare_input(v) for k, v in data.items()})\n elif isinstance(data, (tuple, list)):\n return type(data)(self._prepare_input(v) for v in data)\n elif isinstance(data, torch.Tensor):\n kwargs = {\"device\": self.args.device}\n if self.deepspeed and (torch.is_floating_point(data) or torch.is_complex(data)):\n # NLP models inputs are int/uint and those get adjusted to the right dtype of the\n # embedding. Other models such as wav2vec2's inputs are already float and thus\n # may need special handling to match the dtypes of the model\n kwargs.update({\"dtype\": self.args.hf_deepspeed_config.dtype()})\n return data.to(**kwargs)\n return data\n\n def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:\n \"\"\"\n Prepare `inputs` before feeding them to the model, converting them to tensors if they are not already and\n handling potential state.\n \"\"\"\n inputs = self._prepare_input(inputs)\n if len(inputs) == 0:\n raise ValueError(\n \"The batch received was empty, your model won't be able to train on it. Double-check that your \"\n f\"training dataset contains keys expected by the model: {','.join(self._signature_columns)}.\"\n )\n if self.args.past_index >= 0 and self._past is not None:\n inputs[\"mems\"] = self._past\n\n return inputs\n\n def compute_loss_context_manager(self):\n \"\"\"\n A helper wrapper to group together context managers.\n \"\"\"\n return self.autocast_smart_context_manager()\n\n def autocast_smart_context_manager(self, cache_enabled: Optional[bool] = True):\n \"\"\"\n A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired\n arguments, depending on the situation.\n \"\"\"\n if self.use_cuda_amp or self.use_cpu_amp:\n if is_torch_greater_or_equal_than_1_10:\n ctx_manager = (\n torch.cpu.amp.autocast(cache_enabled=cache_enabled, dtype=self.amp_dtype)\n if self.use_cpu_amp\n else torch.cuda.amp.autocast(cache_enabled=cache_enabled, dtype=self.amp_dtype)\n )\n else:\n ctx_manager = torch.cuda.amp.autocast()\n else:\n ctx_manager = contextlib.nullcontext() if sys.version_info >= (3, 7) else contextlib.suppress()\n\n return ctx_manager\n\n def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:\n \"\"\"\n Perform a training step on a batch of inputs.\n\n Subclass and override to inject custom behavior.\n\n Args:\n model (`nn.Module`):\n The model to train.\n inputs (`Dict[str, Union[torch.Tensor, Any]]`):\n The inputs and targets of the model.\n\n The dictionary will be unpacked before being fed to the model. Most models expect the targets under the\n argument `labels`. Check your model's documentation for all accepted arguments.\n\n Return:\n `torch.Tensor`: The tensor with training loss on this batch.\n \"\"\"\n model.train()\n inputs = self._prepare_inputs(inputs)\n\n if is_sagemaker_mp_enabled():\n loss_mb = smp_forward_backward(model, inputs, self.args.gradient_accumulation_steps)\n return loss_mb.reduce_mean().detach().to(self.args.device)\n\n with self.compute_loss_context_manager():\n loss = self.compute_loss(model, inputs)\n\n if self.args.n_gpu > 1:\n loss = loss.mean() # mean() to average on multi-gpu parallel training\n\n if self.args.gradient_accumulation_steps > 1 and not self.deepspeed:\n # deepspeed handles loss scaling by gradient_accumulation_steps in its `backward`\n loss = loss / self.args.gradient_accumulation_steps\n\n if self.do_grad_scaling:\n self.scaler.scale(loss).backward()\n elif self.use_apex:\n with amp.scale_loss(loss, self.optimizer) as scaled_loss:\n scaled_loss.backward()\n elif self.deepspeed:\n # loss gets scaled under gradient_accumulation_steps in deepspeed\n loss = self.deepspeed.backward(loss)\n else:\n loss.backward()\n\n return loss.detach()\n\n def compute_loss(self, model, inputs, return_outputs=False):\n \"\"\"\n How the loss is computed by Trainer. By default, all models return the loss in the first element.\n\n Subclass and override for custom behavior.\n \"\"\"\n if self.label_smoother is not None and \"labels\" in inputs:\n labels = inputs.pop(\"labels\")\n else:\n labels = None\n outputs = model(**inputs)\n # Save past state if it exists\n # TODO: this needs to be fixed and made cleaner later.\n if self.args.past_index >= 0:\n self._past = outputs[self.args.past_index]\n\n if labels is not None:\n if unwrap_model(model)._get_name() in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values():\n loss = self.label_smoother(outputs, labels, shift_labels=True)\n else:\n loss = self.label_smoother(outputs, labels)\n else:\n if isinstance(outputs, dict) and \"loss\" not in outputs:\n raise ValueError(\n \"The model did not return a loss from the inputs, only the following keys: \"\n f\"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}.\"\n )\n # We don't use .loss here since the model may return tuples instead of ModelOutput.\n loss = outputs[\"loss\"] if isinstance(outputs, dict) else outputs[0]\n\n return (loss, outputs) if return_outputs else loss\n\n def is_local_process_zero(self) -> bool:\n \"\"\"\n Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several\n machines) main process.\n \"\"\"\n return self.args.local_process_index == 0\n\n def is_world_process_zero(self) -> bool:\n \"\"\"\n Whether or not this process is the global main process (when training in a distributed fashion on several\n machines, this is only going to be `True` for one process).\n \"\"\"\n # Special case for SageMaker ModelParallel since there process_index is dp_process_index, not the global\n # process index.\n if is_sagemaker_mp_enabled():\n return smp.rank() == 0\n else:\n return self.args.process_index == 0\n\n def save_model(self, output_dir: Optional[str] = None, _internal_call: bool = False):\n \"\"\"\n Will save the model, so you can reload it using `from_pretrained()`.\n\n Will only save from the main process.\n \"\"\"\n\n if output_dir is None:\n output_dir = self.args.output_dir\n\n if is_torch_tpu_available():\n self._save_tpu(output_dir)\n elif is_sagemaker_mp_enabled():\n # Calling the state_dict needs to be done on the wrapped model and on all processes.\n os.makedirs(output_dir, exist_ok=True)\n state_dict = self.model_wrapped.state_dict()\n if self.args.should_save:\n self._save(output_dir, state_dict=state_dict)\n if IS_SAGEMAKER_MP_POST_1_10:\n # 'user_content.pt' indicates model state_dict saved with smp >= 1.10\n Path(os.path.join(output_dir, \"user_content.pt\")).touch()\n elif (\n ShardedDDPOption.ZERO_DP_2 in self.args.sharded_ddp\n or ShardedDDPOption.ZERO_DP_3 in self.args.sharded_ddp\n or self.fsdp is not None\n ):\n state_dict = self.model.state_dict()\n\n if self.args.should_save:\n self._save(output_dir, state_dict=state_dict)\n elif self.deepspeed:\n # this takes care of everything as long as we aren't under zero3\n if self.args.should_save:\n self._save(output_dir)\n\n if is_deepspeed_zero3_enabled():\n # It's too complicated to try to override different places where the weights dump gets\n # saved, so since under zero3 the file is bogus, simply delete it. The user should\n # either user deepspeed checkpoint to resume or to recover full weights use\n # zero_to_fp32.py stored in the checkpoint.\n if self.args.should_save:\n file = os.path.join(output_dir, WEIGHTS_NAME)\n if os.path.isfile(file):\n # logger.info(f\"deepspeed zero3: removing {file}, see zero_to_fp32.py to recover weights\")\n os.remove(file)\n\n # now save the real model if stage3_gather_16bit_weights_on_model_save=True\n # if false it will not be saved.\n # This must be called on all ranks\n if not self.deepspeed.save_16bit_model(output_dir, WEIGHTS_NAME):\n logger.warning(\n \"deepspeed.save_16bit_model didn't save the model, since\"\n \" stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead, use\"\n \" zero_to_fp32.py to recover weights\"\n )\n self.deepspeed.save_checkpoint(output_dir)\n\n elif self.args.should_save:\n self._save(output_dir)\n\n # Push to the Hub when `save_model` is called by the user.\n if self.args.push_to_hub and not _internal_call:\n self.push_to_hub(commit_message=\"Model save\")\n\n def _save_tpu(self, output_dir: Optional[str] = None):\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n logger.info(f\"Saving model checkpoint to {output_dir}\")\n\n if xm.is_master_ordinal():\n os.makedirs(output_dir, exist_ok=True)\n torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))\n\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n xm.rendezvous(\"saving_checkpoint\")\n if not isinstance(self.model, PreTrainedModel):\n if isinstance(unwrap_model(self.model), PreTrainedModel):\n unwrap_model(self.model).save_pretrained(\n output_dir,\n is_main_process=self.args.should_save,\n state_dict=self.model.state_dict(),\n save_function=xm.save,\n )\n else:\n logger.info(\"Trainer.model is not a `PreTrainedModel`, only saving its state dict.\")\n state_dict = self.model.state_dict()\n xm.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))\n else:\n self.model.save_pretrained(output_dir, is_main_process=self.args.should_save, save_function=xm.save)\n if self.tokenizer is not None and self.args.should_save:\n self.tokenizer.save_pretrained(output_dir)\n\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n # If we are executing this function, we are the process zero, so we don't check for that.\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n os.makedirs(output_dir, exist_ok=True)\n logger.info(f\"Saving model checkpoint to {output_dir}\")\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n if not isinstance(self.model, PreTrainedModel):\n if state_dict is None:\n state_dict = self.model.state_dict()\n\n if isinstance(unwrap_model(self.model), PreTrainedModel):\n unwrap_model(self.model).save_pretrained(\n output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors\n )\n else:\n logger.info(\"Trainer.model is not a `PreTrainedModel`, only saving its state dict.\")\n if self.args.save_safetensors:\n safetensors.torch.save_file(state_dict, os.path.join(output_dir, SAFE_WEIGHTS_NAME))\n else:\n torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))\n else:\n self.model.save_pretrained(\n output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors\n )\n\n if self.tokenizer is not None:\n self.tokenizer.save_pretrained(output_dir)\n\n # Good practice: save your training arguments together with the trained model\n torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))\n\n def store_flos(self):\n # Storing the number of floating-point operations that went into the model\n if self.args.local_rank != -1:\n self.state.total_flos += (\n distributed_broadcast_scalars([self.current_flos], device=self.args.device).sum().item()\n )\n self.current_flos = 0\n else:\n self.state.total_flos += self.current_flos\n self.current_flos = 0\n\n def _sorted_checkpoints(\n self, output_dir=None, checkpoint_prefix=PREFIX_CHECKPOINT_DIR, use_mtime=False\n ) -> List[str]:\n ordering_and_checkpoint_path = []\n\n glob_checkpoints = [str(x) for x in Path(output_dir).glob(f\"{checkpoint_prefix}-*\") if os.path.isdir(x)]\n\n for path in glob_checkpoints:\n if use_mtime:\n ordering_and_checkpoint_path.append((os.path.getmtime(path), path))\n else:\n regex_match = re.match(f\".*{checkpoint_prefix}-([0-9]+)\", path)\n if regex_match is not None and regex_match.groups() is not None:\n ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))\n\n checkpoints_sorted = sorted(ordering_and_checkpoint_path)\n checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]\n # Make sure we don't delete the best model.\n if self.state.best_model_checkpoint is not None:\n best_model_index = checkpoints_sorted.index(str(Path(self.state.best_model_checkpoint)))\n for i in range(best_model_index, len(checkpoints_sorted) - 2):\n checkpoints_sorted[i], checkpoints_sorted[i + 1] = checkpoints_sorted[i + 1], checkpoints_sorted[i]\n return checkpoints_sorted\n\n def _rotate_checkpoints(self, use_mtime=False, output_dir=None) -> None:\n if self.args.save_total_limit is None or self.args.save_total_limit <= 0:\n return\n\n # Check if we should delete older checkpoint(s)\n checkpoints_sorted = self._sorted_checkpoints(use_mtime=use_mtime, output_dir=output_dir)\n if len(checkpoints_sorted) <= self.args.save_total_limit:\n return\n\n # If save_total_limit=1 with load_best_model_at_end=True, we could end up deleting the last checkpoint, which\n # we don't do to allow resuming.\n save_total_limit = self.args.save_total_limit\n if (\n self.state.best_model_checkpoint is not None\n and self.args.save_total_limit == 1\n and checkpoints_sorted[-1] != self.state.best_model_checkpoint\n ):\n save_total_limit = 2\n\n number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - save_total_limit)\n checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]\n for checkpoint in checkpoints_to_be_deleted:\n logger.info(f\"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit\")\n shutil.rmtree(checkpoint, ignore_errors=True)\n\n def evaluate(\n self,\n eval_dataset: Optional[Dataset] = None,\n ignore_keys: Optional[List[str]] = None,\n metric_key_prefix: str = \"eval\",\n ) -> Dict[str, float]:\n \"\"\"\n Run evaluation and returns metrics.\n\n The calling script will be responsible for providing a method to compute metrics, as they are task-dependent\n (pass it to the init `compute_metrics` argument).\n\n You can also subclass and override this method to inject custom behavior.\n\n Args:\n eval_dataset (`Dataset`, *optional*):\n Pass a dataset if you wish to override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns\n not accepted by the `model.forward()` method are automatically removed. It must implement the `__len__`\n method.\n ignore_keys (`List[str]`, *optional*):\n A list of keys in the output of your model (if it is a dictionary) that should be ignored when\n gathering predictions.\n metric_key_prefix (`str`, *optional*, defaults to `\"eval\"`):\n An optional prefix to be used as the metrics key prefix. For example the metrics \"bleu\" will be named\n \"eval_bleu\" if the prefix is \"eval\" (default)\n\n Returns:\n A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The\n dictionary also contains the epoch number which comes from the training state.\n \"\"\"\n # memory metrics - must set up as early as possible\n self._memory_tracker.start()\n\n eval_dataloader = self.get_eval_dataloader(eval_dataset)\n start_time = time.time()\n\n eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop\n output = eval_loop(\n eval_dataloader,\n description=\"Evaluation\",\n # No point gathering the predictions if there are no metrics, otherwise we defer to\n # self.args.prediction_loss_only\n prediction_loss_only=True if self.compute_metrics is None else None,\n ignore_keys=ignore_keys,\n metric_key_prefix=metric_key_prefix,\n )\n\n total_batch_size = self.args.eval_batch_size * self.args.world_size\n if f\"{metric_key_prefix}_jit_compilation_time\" in output.metrics:\n start_time += output.metrics[f\"{metric_key_prefix}_jit_compilation_time\"]\n output.metrics.update(\n speed_metrics(\n metric_key_prefix,\n start_time,\n num_samples=output.num_samples,\n num_steps=math.ceil(output.num_samples / total_batch_size),\n )\n )\n\n self.log(output.metrics)\n\n if DebugOption.TPU_METRICS_DEBUG in self.args.debug:\n # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)\n xm.master_print(met.metrics_report())\n\n self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, output.metrics)\n\n self._memory_tracker.stop_and_update_metrics(output.metrics)\n\n return output.metrics\n\n def predict(\n self, test_dataset: Dataset, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = \"test\"\n ) -> PredictionOutput:\n \"\"\"\n Run prediction and returns predictions and potential metrics.\n\n Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method\n will also return metrics, like in `evaluate()`.\n\n Args:\n test_dataset (`Dataset`):\n Dataset to run the predictions on. If it is an `datasets.Dataset`, columns not accepted by the\n `model.forward()` method are automatically removed. Has to implement the method `__len__`\n ignore_keys (`List[str]`, *optional*):\n A list of keys in the output of your model (if it is a dictionary) that should be ignored when\n gathering predictions.\n metric_key_prefix (`str`, *optional*, defaults to `\"test\"`):\n An optional prefix to be used as the metrics key prefix. For example the metrics \"bleu\" will be named\n \"test_bleu\" if the prefix is \"test\" (default)\n\n \n\n If your predictions or labels have different sequence length (for instance because you're doing dynamic padding\n in a token classification task) the predictions will be padded (on the right) to allow for concatenation into\n one array. The padding index is -100.\n\n \n\n Returns: *NamedTuple* A namedtuple with the following keys:\n\n - predictions (`np.ndarray`): The predictions on `test_dataset`.\n - label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some).\n - metrics (`Dict[str, float]`, *optional*): The potential dictionary of metrics (if the dataset contained\n labels).\n \"\"\"\n # memory metrics - must set up as early as possible\n self._memory_tracker.start()\n\n test_dataloader = self.get_test_dataloader(test_dataset)\n start_time = time.time()\n\n eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop\n output = eval_loop(\n test_dataloader, description=\"Prediction\", ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix\n )\n total_batch_size = self.args.eval_batch_size * self.args.world_size\n if f\"{metric_key_prefix}_jit_compilation_time\" in output.metrics:\n start_time += output.metrics[f\"{metric_key_prefix}_jit_compilation_time\"]\n output.metrics.update(\n speed_metrics(\n metric_key_prefix,\n start_time,\n num_samples=output.num_samples,\n num_steps=math.ceil(output.num_samples / total_batch_size),\n )\n )\n\n self.control = self.callback_handler.on_predict(self.args, self.state, self.control, output.metrics)\n self._memory_tracker.stop_and_update_metrics(output.metrics)\n\n return PredictionOutput(predictions=output.predictions, label_ids=output.label_ids, metrics=output.metrics)\n\n def evaluation_loop(\n self,\n dataloader: DataLoader,\n description: str,\n prediction_loss_only: Optional[bool] = None,\n ignore_keys: Optional[List[str]] = None,\n metric_key_prefix: str = \"eval\",\n ) -> EvalLoopOutput:\n \"\"\"\n Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.\n\n Works both with or without labels.\n \"\"\"\n args = self.args\n\n prediction_loss_only = prediction_loss_only if prediction_loss_only is not None else args.prediction_loss_only\n\n # if eval is called w/o train init deepspeed here\n if args.deepspeed and not self.deepspeed:\n # XXX: eval doesn't have `resume_from_checkpoint` arg but we should be able to do eval\n # from the checkpoint eventually\n deepspeed_engine, _, _ = deepspeed_init(\n self, num_training_steps=0, resume_from_checkpoint=None, inference=True\n )\n self.model = deepspeed_engine.module\n self.model_wrapped = deepspeed_engine\n self.deepspeed = deepspeed_engine\n\n model = self._wrap_model(self.model, training=False, dataloader=dataloader)\n\n # if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called\n # while ``train`` is running, cast it to the right dtype first and then put on device\n if not self.is_in_train:\n if args.fp16_full_eval:\n model = model.to(dtype=torch.float16, device=args.device)\n elif args.bf16_full_eval:\n model = model.to(dtype=torch.bfloat16, device=args.device)\n\n batch_size = self.args.eval_batch_size\n\n logger.info(f\"***** Running {description} *****\")\n if has_length(dataloader):\n logger.info(f\" Num examples = {self.num_examples(dataloader)}\")\n else:\n logger.info(\" Num examples: Unknown\")\n logger.info(f\" Batch size = {batch_size}\")\n\n model.eval()\n\n self.callback_handler.eval_dataloader = dataloader\n # Do this before wrapping.\n eval_dataset = getattr(dataloader, \"dataset\", None)\n\n if is_torch_tpu_available():\n dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)\n\n if args.past_index >= 0:\n self._past = None\n\n # Initialize containers\n # losses/preds/labels on GPU/TPU (accumulated for eval_accumulation_steps)\n losses_host = None\n preds_host = None\n labels_host = None\n inputs_host = None\n\n # losses/preds/labels on CPU (final containers)\n all_losses = None\n all_preds = None\n all_labels = None\n all_inputs = None\n # Will be useful when we have an iterable dataset so don't know its length.\n\n observed_num_examples = 0\n # Main evaluation loop\n for step, inputs in enumerate(dataloader):\n # Update the observed num examples\n observed_batch_size = find_batch_size(inputs)\n if observed_batch_size is not None:\n observed_num_examples += observed_batch_size\n # For batch samplers, batch_size is not known by the dataloader in advance.\n if batch_size is None:\n batch_size = observed_batch_size\n\n # Prediction step\n loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)\n inputs_decode = self._prepare_input(inputs[\"input_ids\"]) if args.include_inputs_for_metrics else None\n\n if is_torch_tpu_available():\n xm.mark_step()\n\n # Update containers on host\n if loss is not None:\n losses = self._nested_gather(loss.repeat(batch_size))\n losses_host = losses if losses_host is None else torch.cat((losses_host, losses), dim=0)\n if labels is not None:\n labels = self._pad_across_processes(labels)\n labels = self._nested_gather(labels)\n labels_host = labels if labels_host is None else nested_concat(labels_host, labels, padding_index=-100)\n if inputs_decode is not None:\n inputs_decode = self._pad_across_processes(inputs_decode)\n inputs_decode = self._nested_gather(inputs_decode)\n inputs_host = (\n inputs_decode\n if inputs_host is None\n else nested_concat(inputs_host, inputs_decode, padding_index=-100)\n )\n if logits is not None:\n logits = self._pad_across_processes(logits)\n logits = self._nested_gather(logits)\n if self.preprocess_logits_for_metrics is not None:\n logits = self.preprocess_logits_for_metrics(logits, labels)\n preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)\n self.control = self.callback_handler.on_prediction_step(args, self.state, self.control)\n\n # Gather all tensors and put them back on the CPU if we have done enough accumulation steps.\n if args.eval_accumulation_steps is not None and (step + 1) % args.eval_accumulation_steps == 0:\n if losses_host is not None:\n losses = nested_numpify(losses_host)\n all_losses = losses if all_losses is None else np.concatenate((all_losses, losses), axis=0)\n if preds_host is not None:\n logits = nested_numpify(preds_host)\n all_preds = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)\n if inputs_host is not None:\n inputs_decode = nested_numpify(inputs_host)\n all_inputs = (\n inputs_decode\n if all_inputs is None\n else nested_concat(all_inputs, inputs_decode, padding_index=-100)\n )\n if labels_host is not None:\n labels = nested_numpify(labels_host)\n all_labels = (\n labels if all_labels is None else nested_concat(all_labels, labels, padding_index=-100)\n )\n\n # Set back to None to begin a new accumulation\n losses_host, preds_host, inputs_host, labels_host = None, None, None, None\n\n if args.past_index and hasattr(self, \"_past\"):\n # Clean the state at the end of the evaluation loop\n delattr(self, \"_past\")\n\n # Gather all remaining tensors and put them back on the CPU\n if losses_host is not None:\n losses = nested_numpify(losses_host)\n all_losses = losses if all_losses is None else np.concatenate((all_losses, losses), axis=0)\n if preds_host is not None:\n logits = nested_numpify(preds_host)\n all_preds = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)\n if inputs_host is not None:\n inputs_decode = nested_numpify(inputs_host)\n all_inputs = (\n inputs_decode if all_inputs is None else nested_concat(all_inputs, inputs_decode, padding_index=-100)\n )\n if labels_host is not None:\n labels = nested_numpify(labels_host)\n all_labels = labels if all_labels is None else nested_concat(all_labels, labels, padding_index=-100)\n\n # Number of samples\n if has_length(eval_dataset):\n num_samples = len(eval_dataset)\n # The instance check is weird and does not actually check for the type, but whether the dataset has the right\n # methods. Therefore we need to make sure it also has the attribute.\n elif isinstance(eval_dataset, IterableDatasetShard) and getattr(eval_dataset, \"num_examples\", 0) > 0:\n num_samples = eval_dataset.num_examples\n else:\n if has_length(dataloader):\n num_samples = self.num_examples(dataloader)\n else: # both len(dataloader.dataset) and len(dataloader) fail\n num_samples = observed_num_examples\n if num_samples == 0 and observed_num_examples > 0:\n num_samples = observed_num_examples\n\n # Number of losses has been rounded to a multiple of batch_size and in a distributed training, the number of\n # samplers has been rounded to a multiple of batch_size, so we truncate.\n if all_losses is not None:\n all_losses = all_losses[:num_samples]\n if all_preds is not None:\n all_preds = nested_truncate(all_preds, num_samples)\n if all_labels is not None:\n all_labels = nested_truncate(all_labels, num_samples)\n if all_inputs is not None:\n all_inputs = nested_truncate(all_inputs, num_samples)\n\n # Metrics!\n if self.compute_metrics is not None and all_preds is not None and all_labels is not None:\n if args.include_inputs_for_metrics:\n metrics = self.compute_metrics(\n EvalPrediction(predictions=all_preds, label_ids=all_labels, inputs=all_inputs)\n )\n else:\n metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))\n else:\n metrics = {}\n\n # To be JSON-serializable, we need to remove numpy types or zero-d tensors\n metrics = denumpify_detensorize(metrics)\n\n if all_losses is not None:\n metrics[f\"{metric_key_prefix}_loss\"] = all_losses.mean().item()\n if hasattr(self, \"jit_compilation_time\"):\n metrics[f\"{metric_key_prefix}_jit_compilation_time\"] = self.jit_compilation_time\n\n # Prefix all keys with metric_key_prefix + '_'\n for key in list(metrics.keys()):\n if not key.startswith(f\"{metric_key_prefix}_\"):\n metrics[f\"{metric_key_prefix}_{key}\"] = metrics.pop(key)\n\n return EvalLoopOutput(predictions=all_preds, label_ids=all_labels, metrics=metrics, num_samples=num_samples)\n\n def _nested_gather(self, tensors, name=None):\n \"\"\"\n Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before\n concatenating them to `gathered`\n \"\"\"\n if tensors is None:\n return\n if is_torch_tpu_available():\n if name is None:\n name = \"nested_gather\"\n tensors = nested_xla_mesh_reduce(tensors, name)\n elif is_sagemaker_mp_enabled():\n tensors = smp_gather(tensors)\n elif self.args.local_rank != -1:\n tensors = distributed_concat(tensors)\n return tensors\n\n # Copied from Accelerate.\n def _pad_across_processes(self, tensor, pad_index=-100):\n \"\"\"\n Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so\n they can safely be gathered.\n \"\"\"\n if isinstance(tensor, (list, tuple)):\n return type(tensor)(self._pad_across_processes(t, pad_index=pad_index) for t in tensor)\n elif isinstance(tensor, dict):\n return type(tensor)({k: self._pad_across_processes(v, pad_index=pad_index) for k, v in tensor.items()})\n elif not isinstance(tensor, torch.Tensor):\n raise TypeError(\n f\"Can't pad the values of type {type(tensor)}, only of nested list/tuple/dicts of tensors.\"\n )\n\n if len(tensor.shape) < 2:\n return tensor\n # Gather all sizes\n size = torch.tensor(tensor.shape, device=tensor.device)[None]\n sizes = self._nested_gather(size).cpu()\n\n max_size = max(s[1] for s in sizes)\n # When extracting XLA graphs for compilation, max_size is 0,\n # so use inequality to avoid errors.\n if tensor.shape[1] >= max_size:\n return tensor\n\n # Then pad to the maximum size\n old_size = tensor.shape\n new_size = list(old_size)\n new_size[1] = max_size\n new_tensor = tensor.new_zeros(tuple(new_size)) + pad_index\n new_tensor[:, : old_size[1]] = tensor\n return new_tensor\n\n def prediction_step(\n self,\n model: nn.Module,\n inputs: Dict[str, Union[torch.Tensor, Any]],\n prediction_loss_only: bool,\n ignore_keys: Optional[List[str]] = None,\n ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:\n \"\"\"\n Perform an evaluation step on `model` using `inputs`.\n\n Subclass and override to inject custom behavior.\n\n Args:\n model (`nn.Module`):\n The model to evaluate.\n inputs (`Dict[str, Union[torch.Tensor, Any]]`):\n The inputs and targets of the model.\n\n The dictionary will be unpacked before being fed to the model. Most models expect the targets under the\n argument `labels`. Check your model's documentation for all accepted arguments.\n prediction_loss_only (`bool`):\n Whether or not to return the loss only.\n ignore_keys (`List[str]`, *optional*):\n A list of keys in the output of your model (if it is a dictionary) that should be ignored when\n gathering predictions.\n\n Return:\n Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss,\n logits and labels (each being optional).\n \"\"\"\n has_labels = False if len(self.label_names) == 0 else all(inputs.get(k) is not None for k in self.label_names)\n # For CLIP-like models capable of returning loss values.\n # If `return_loss` is not specified or being `None` in `inputs`, we check if the default value of `return_loss`\n # is `True` in `model.forward`.\n return_loss = inputs.get(\"return_loss\", None)\n if return_loss is None:\n return_loss = self.can_return_loss\n loss_without_labels = True if len(self.label_names) == 0 and return_loss else False\n\n inputs = self._prepare_inputs(inputs)\n if ignore_keys is None:\n if hasattr(self.model, \"config\"):\n ignore_keys = getattr(self.model.config, \"keys_to_ignore_at_inference\", [])\n else:\n ignore_keys = []\n\n # labels may be popped when computing the loss (label smoothing for instance) so we grab them first.\n if has_labels or loss_without_labels:\n labels = nested_detach(tuple(inputs.get(name) for name in self.label_names))\n if len(labels) == 1:\n labels = labels[0]\n else:\n labels = None\n\n with torch.no_grad():\n if is_sagemaker_mp_enabled():\n raw_outputs = smp_forward_only(model, inputs)\n if has_labels or loss_without_labels:\n if isinstance(raw_outputs, dict):\n loss_mb = raw_outputs[\"loss\"]\n logits_mb = tuple(v for k, v in raw_outputs.items() if k not in ignore_keys + [\"loss\"])\n else:\n loss_mb = raw_outputs[0]\n logits_mb = raw_outputs[1:]\n\n loss = loss_mb.reduce_mean().detach().cpu()\n logits = smp_nested_concat(logits_mb)\n else:\n loss = None\n if isinstance(raw_outputs, dict):\n logits_mb = tuple(v for k, v in raw_outputs.items() if k not in ignore_keys)\n else:\n logits_mb = raw_outputs\n logits = smp_nested_concat(logits_mb)\n else:\n if has_labels or loss_without_labels:\n with self.compute_loss_context_manager():\n loss, outputs = self.compute_loss(model, inputs, return_outputs=True)\n loss = loss.mean().detach()\n\n if isinstance(outputs, dict):\n logits = tuple(v for k, v in outputs.items() if k not in ignore_keys + [\"loss\"])\n else:\n logits = outputs[1:]\n else:\n loss = None\n with self.compute_loss_context_manager():\n outputs = model(**inputs)\n if isinstance(outputs, dict):\n logits = tuple(v for k, v in outputs.items() if k not in ignore_keys)\n else:\n logits = outputs\n # TODO: this needs to be fixed and made cleaner later.\n if self.args.past_index >= 0:\n self._past = outputs[self.args.past_index - 1]\n\n if prediction_loss_only:\n return (loss, None, None)\n\n logits = nested_detach(logits)\n if len(logits) == 1:\n logits = logits[0]\n\n return (loss, logits, labels)\n\n def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any]]):\n \"\"\"\n For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point\n operations for every backward + forward pass. If using another model, either implement such a method in the\n model or subclass and override this method.\n\n Args:\n inputs (`Dict[str, Union[torch.Tensor, Any]]`):\n The inputs and targets of the model.\n\n Returns:\n `int`: The number of floating-point operations.\n \"\"\"\n if hasattr(self.model, \"floating_point_ops\"):\n return self.model.floating_point_ops(inputs)\n else:\n return 0\n\n def init_git_repo(self, at_init: bool = False):\n \"\"\"\n Initializes a git repo in `self.args.hub_model_id`.\n\n Args:\n at_init (`bool`, *optional*, defaults to `False`):\n Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is\n `True` and `at_init` is `True`, the path to the repo (which is `self.args.output_dir`) might be wiped\n out.\n \"\"\"\n if not self.is_world_process_zero():\n return\n if self.args.hub_model_id is None:\n repo_name = Path(self.args.output_dir).absolute().name\n else:\n repo_name = self.args.hub_model_id\n if \"/\" not in repo_name:\n repo_name = get_full_repo_name(repo_name, token=self.args.hub_token)\n\n # Make sure the repo exists.\n create_repo(repo_name, token=self.args.hub_token, private=self.args.hub_private_repo, exist_ok=True)\n try:\n self.repo = Repository(self.args.output_dir, clone_from=repo_name, token=self.args.hub_token)\n except EnvironmentError:\n if self.args.overwrite_output_dir and at_init:\n # Try again after wiping output_dir\n shutil.rmtree(self.args.output_dir)\n self.repo = Repository(self.args.output_dir, clone_from=repo_name, token=self.args.hub_token)\n else:\n raise\n\n self.repo.git_pull()\n\n # By default, ignore the checkpoint folders\n if (\n not os.path.exists(os.path.join(self.args.output_dir, \".gitignore\"))\n and self.args.hub_strategy != HubStrategy.ALL_CHECKPOINTS\n ):\n with open(os.path.join(self.args.output_dir, \".gitignore\"), \"w\", encoding=\"utf-8\") as writer:\n writer.writelines([\"checkpoint-*/\"])\n\n # Add \"*.sagemaker\" to .gitignore if using SageMaker\n if os.environ.get(\"SM_TRAINING_ENV\"):\n self._add_sm_patterns_to_gitignore()\n\n self.push_in_progress = None\n\n def create_model_card(\n self,\n language: Optional[str] = None,\n license: Optional[str] = None,\n tags: Union[str, List[str], None] = None,\n model_name: Optional[str] = None,\n finetuned_from: Optional[str] = None,\n tasks: Union[str, List[str], None] = None,\n dataset_tags: Union[str, List[str], None] = None,\n dataset: Union[str, List[str], None] = None,\n dataset_args: Union[str, List[str], None] = None,\n ):\n \"\"\"\n Creates a draft of a model card using the information available to the `Trainer`.\n\n Args:\n language (`str`, *optional*):\n The language of the model (if applicable)\n license (`str`, *optional*):\n The license of the model. Will default to the license of the pretrained model used, if the original\n model given to the `Trainer` comes from a repo on the Hub.\n tags (`str` or `List[str]`, *optional*):\n Some tags to be included in the metadata of the model card.\n model_name (`str`, *optional*):\n The name of the model.\n finetuned_from (`str`, *optional*):\n The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo\n of the original model given to the `Trainer` (if it comes from the Hub).\n tasks (`str` or `List[str]`, *optional*):\n One or several task identifiers, to be included in the metadata of the model card.\n dataset_tags (`str` or `List[str]`, *optional*):\n One or several dataset tags, to be included in the metadata of the model card.\n dataset (`str` or `List[str]`, *optional*):\n One or several dataset identifiers, to be included in the metadata of the model card.\n dataset_args (`str` or `List[str]`, *optional*):\n One or several dataset arguments, to be included in the metadata of the model card.\n \"\"\"\n if not self.is_world_process_zero():\n return\n\n training_summary = TrainingSummary.from_trainer(\n self,\n language=language,\n license=license,\n tags=tags,\n model_name=model_name,\n finetuned_from=finetuned_from,\n tasks=tasks,\n dataset_tags=dataset_tags,\n dataset=dataset,\n dataset_args=dataset_args,\n )\n model_card = training_summary.to_model_card()\n with open(os.path.join(self.args.output_dir, \"README.md\"), \"w\") as f:\n f.write(model_card)\n\n def _push_from_checkpoint(self, checkpoint_folder):\n # Only push from one node.\n if not self.is_world_process_zero() or self.args.hub_strategy == HubStrategy.END:\n return\n # If we haven't finished the last push, we don't do this one.\n if self.push_in_progress is not None and not self.push_in_progress.is_done:\n return\n\n output_dir = self.args.output_dir\n # To avoid a new synchronization of all model weights, we just copy the file from the checkpoint folder\n modeling_files = [CONFIG_NAME, WEIGHTS_NAME, SAFE_WEIGHTS_NAME]\n for modeling_file in modeling_files:\n if os.path.isfile(os.path.join(checkpoint_folder, modeling_file)):\n shutil.copy(os.path.join(checkpoint_folder, modeling_file), os.path.join(output_dir, modeling_file))\n # Saving the tokenizer is fast and we don't know how many files it may have spawned, so we resave it to be sure.\n if self.tokenizer is not None:\n self.tokenizer.save_pretrained(output_dir)\n # Same for the training arguments\n torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))\n\n try:\n if self.args.hub_strategy == HubStrategy.CHECKPOINT:\n # Temporarily move the checkpoint just saved for the push\n tmp_checkpoint = os.path.join(output_dir, \"last-checkpoint\")\n # We have to remove the \"last-checkpoint\" dir if it exists, otherwise the checkpoint is moved as a\n # subfolder.\n if os.path.isdir(tmp_checkpoint):\n shutil.rmtree(tmp_checkpoint)\n shutil.move(checkpoint_folder, tmp_checkpoint)\n\n if self.args.save_strategy == IntervalStrategy.STEPS:\n commit_message = f\"Training in progress, step {self.state.global_step}\"\n else:\n commit_message = f\"Training in progress, epoch {int(self.state.epoch)}\"\n _, self.push_in_progress = self.repo.push_to_hub(\n commit_message=commit_message, blocking=False, auto_lfs_prune=True\n )\n finally:\n if self.args.hub_strategy == HubStrategy.CHECKPOINT:\n # Move back the checkpoint to its place\n shutil.move(tmp_checkpoint, checkpoint_folder)\n\n def push_to_hub(self, commit_message: Optional[str] = \"End of training\", blocking: bool = True, **kwargs) -> str:\n \"\"\"\n Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*.\n\n Parameters:\n commit_message (`str`, *optional*, defaults to `\"End of training\"`):\n Message to commit while pushing.\n blocking (`bool`, *optional*, defaults to `True`):\n Whether the function should return only when the `git push` has finished.\n kwargs:\n Additional keyword arguments passed along to [`~Trainer.create_model_card`].\n\n Returns:\n The url of the commit of your model in the given repository if `blocking=False`, a tuple with the url of\n the commit and an object to track the progress of the commit if `blocking=True`\n \"\"\"\n # If a user calls manually `push_to_hub` with `self.args.push_to_hub = False`, we try to create the repo but\n # it might fail.\n if not hasattr(self, \"repo\"):\n self.init_git_repo()\n\n model_name = kwargs.pop(\"model_name\", None)\n if model_name is None and self.args.should_save:\n if self.args.hub_model_id is None:\n model_name = Path(self.args.output_dir).name\n else:\n model_name = self.args.hub_model_id.split(\"/\")[-1]\n\n # Needs to be executed on all processes for TPU training, but will only save on the processed determined by\n # self.args.should_save.\n self.save_model(_internal_call=True)\n\n # Only push from one node.\n if not self.is_world_process_zero():\n return\n\n # Cancel any async push in progress if blocking=True. The commits will all be pushed together.\n if blocking and self.push_in_progress is not None and not self.push_in_progress.is_done:\n self.push_in_progress._process.kill()\n self.push_in_progress = None\n\n git_head_commit_url = self.repo.push_to_hub(\n commit_message=commit_message, blocking=blocking, auto_lfs_prune=True\n )\n # push separately the model card to be independant from the rest of the model\n if self.args.should_save:\n self.create_model_card(model_name=model_name, **kwargs)\n try:\n self.repo.push_to_hub(\n commit_message=\"update model card README.md\", blocking=blocking, auto_lfs_prune=True\n )\n except EnvironmentError as exc:\n logger.error(f\"Error pushing update to the model card. Please read logs and retry.\\n${exc}\")\n\n return git_head_commit_url\n\n #\n # Deprecated code\n #\n\n def prediction_loop(\n self,\n dataloader: DataLoader,\n description: str,\n prediction_loss_only: Optional[bool] = None,\n ignore_keys: Optional[List[str]] = None,\n metric_key_prefix: str = \"eval\",\n ) -> EvalLoopOutput:\n \"\"\"\n Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.\n\n Works both with or without labels.\n \"\"\"\n args = self.args\n\n if not has_length(dataloader):\n raise ValueError(\"dataloader must implement a working __len__\")\n\n prediction_loss_only = prediction_loss_only if prediction_loss_only is not None else args.prediction_loss_only\n\n # if eval is called w/o train init deepspeed here\n if args.deepspeed and not self.deepspeed:\n # XXX: eval doesn't have `resume_from_checkpoint` arg but we should be able to do eval\n # from the checkpoint eventually\n deepspeed_engine, _, _ = deepspeed_init(self, num_training_steps=0, resume_from_checkpoint=None)\n self.model = deepspeed_engine.module\n self.model_wrapped = deepspeed_engine\n self.deepspeed = deepspeed_engine\n # XXX: we don't need optim/sched for inference, but this needs to be sorted out, since\n # for example the Z3-optimizer is a must for zero3 to work even for inference - what we\n # don't need is the deepspeed basic optimizer which is self.optimizer.optimizer\n deepspeed_engine.optimizer.optimizer = None\n deepspeed_engine.lr_scheduler = None\n\n model = self._wrap_model(self.model, training=False, dataloader=dataloader)\n\n # if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called\n # while ``train`` is running, cast it to the right dtype first and then put on device\n if not self.is_in_train:\n if args.fp16_full_eval:\n model = model.to(dtype=torch.float16, device=args.device)\n elif args.bf16_full_eval:\n model = model.to(dtype=torch.bfloat16, device=args.device)\n\n batch_size = dataloader.batch_size\n num_examples = self.num_examples(dataloader)\n logger.info(f\"***** Running {description} *****\")\n logger.info(f\" Num examples = {num_examples}\")\n logger.info(f\" Batch size = {batch_size}\")\n losses_host: torch.Tensor = None\n preds_host: Union[torch.Tensor, List[torch.Tensor]] = None\n labels_host: Union[torch.Tensor, List[torch.Tensor]] = None\n inputs_host: Union[torch.Tensor, List[torch.Tensor]] = None\n\n world_size = max(1, args.world_size)\n\n eval_losses_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=batch_size)\n if not prediction_loss_only:\n # The actual number of eval_sample can be greater than num_examples in distributed settings (when we pass\n # a batch size to the sampler)\n make_multiple_of = None\n if hasattr(dataloader, \"sampler\") and isinstance(dataloader.sampler, SequentialDistributedSampler):\n make_multiple_of = dataloader.sampler.batch_size\n preds_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=make_multiple_of)\n labels_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=make_multiple_of)\n inputs_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=make_multiple_of)\n\n model.eval()\n\n if is_torch_tpu_available():\n dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)\n\n if args.past_index >= 0:\n self._past = None\n\n self.callback_handler.eval_dataloader = dataloader\n\n for step, inputs in enumerate(dataloader):\n loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)\n inputs_decode = self._prepare_input(inputs[\"input_ids\"]) if args.include_inputs_for_metrics else None\n\n if loss is not None:\n losses = loss.repeat(batch_size)\n losses_host = losses if losses_host is None else torch.cat((losses_host, losses), dim=0)\n if logits is not None:\n preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)\n if labels is not None:\n labels_host = labels if labels_host is None else nested_concat(labels_host, labels, padding_index=-100)\n if inputs_decode is not None:\n inputs_host = (\n inputs_decode\n if inputs_host is None\n else nested_concat(inputs_host, inputs_decode, padding_index=-100)\n )\n self.control = self.callback_handler.on_prediction_step(args, self.state, self.control)\n\n # Gather all tensors and put them back on the CPU if we have done enough accumulation steps.\n if args.eval_accumulation_steps is not None and (step + 1) % args.eval_accumulation_steps == 0:\n eval_losses_gatherer.add_arrays(self._gather_and_numpify(losses_host, \"eval_losses\"))\n if not prediction_loss_only:\n preds_gatherer.add_arrays(self._gather_and_numpify(preds_host, \"eval_preds\"))\n labels_gatherer.add_arrays(self._gather_and_numpify(labels_host, \"eval_label_ids\"))\n inputs_gatherer.add_arrays(self._gather_and_numpify(inputs_host, \"eval_inputs_ids\"))\n\n # Set back to None to begin a new accumulation\n losses_host, preds_host, labels_host, inputs_host = None, None, None, None\n\n if args.past_index and hasattr(self, \"_past\"):\n # Clean the state at the end of the evaluation loop\n delattr(self, \"_past\")\n\n # Gather all remaining tensors and put them back on the CPU\n eval_losses_gatherer.add_arrays(self._gather_and_numpify(losses_host, \"eval_losses\"))\n if not prediction_loss_only:\n preds_gatherer.add_arrays(self._gather_and_numpify(preds_host, \"eval_preds\"))\n labels_gatherer.add_arrays(self._gather_and_numpify(labels_host, \"eval_label_ids\"))\n inputs_gatherer.add_arrays(self._gather_and_numpify(inputs_host, \"eval_inputs_ids\"))\n\n eval_loss = eval_losses_gatherer.finalize()\n preds = preds_gatherer.finalize() if not prediction_loss_only else None\n label_ids = labels_gatherer.finalize() if not prediction_loss_only else None\n inputs_ids = inputs_gatherer.finalize() if not prediction_loss_only else None\n\n if self.compute_metrics is not None and preds is not None and label_ids is not None:\n if args.include_inputs_for_metrics:\n metrics = self.compute_metrics(\n EvalPrediction(predictions=preds, label_ids=label_ids, inputs=inputs_ids)\n )\n else:\n metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\n else:\n metrics = {}\n\n # To be JSON-serializable, we need to remove numpy types or zero-d tensors\n metrics = denumpify_detensorize(metrics)\n\n if eval_loss is not None:\n metrics[f\"{metric_key_prefix}_loss\"] = eval_loss.mean().item()\n\n # Prefix all keys with metric_key_prefix + '_'\n for key in list(metrics.keys()):\n if not key.startswith(f\"{metric_key_prefix}_\"):\n metrics[f\"{metric_key_prefix}_{key}\"] = metrics.pop(key)\n\n return EvalLoopOutput(predictions=preds, label_ids=label_ids, metrics=metrics, num_samples=num_examples)\n\n def _gather_and_numpify(self, tensors, name):\n \"\"\"\n Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before\n concatenating them to `gathered`\n \"\"\"\n if tensors is None:\n return\n if is_torch_tpu_available():\n tensors = nested_xla_mesh_reduce(tensors, name)\n elif is_sagemaker_mp_enabled():\n tensors = smp_gather(tensors)\n elif self.args.local_rank != -1:\n tensors = distributed_concat(tensors)\n\n return nested_numpify(tensors)\n\n def _add_sm_patterns_to_gitignore(self) -> None:\n \"\"\"Add SageMaker Checkpointing patterns to .gitignore file.\"\"\"\n # Make sure we only do this on the main process\n if not self.is_world_process_zero():\n return\n\n patterns = [\"*.sagemaker-uploading\", \"*.sagemaker-uploaded\"]\n\n # Get current .gitignore content\n if os.path.exists(os.path.join(self.repo.local_dir, \".gitignore\")):\n with open(os.path.join(self.repo.local_dir, \".gitignore\"), \"r\") as f:\n current_content = f.read()\n else:\n current_content = \"\"\n\n # Add the patterns to .gitignore\n content = current_content\n for pattern in patterns:\n if pattern not in content:\n if content.endswith(\"\\n\"):\n content += pattern\n else:\n content += f\"\\n{pattern}\"\n\n # Write the .gitignore file if it has changed\n if content != current_content:\n with open(os.path.join(self.repo.local_dir, \".gitignore\"), \"w\") as f:\n logger.debug(f\"Writing .gitignore file. Content: {content}\")\n f.write(content)\n\n self.repo.git_add(\".gitignore\")\n\n # avoid race condition with git status\n time.sleep(0.5)\n\n if not self.repo.is_repo_clean():\n self.repo.git_commit(\"Add *.sagemaker patterns to .gitignore.\")\n self.repo.git_push()\n"
},
{
"path": "src/datasampler.py",
"content": "import torch.distributed as dist\nimport math\nfrom torch.utils.data.sampler import Sampler\nfrom torch.utils.data.sampler import Sampler\nfrom torch.utils.data import DataLoader, DistributedSampler\nimport random\nimport torch\nfrom Dataset.multi_dataset import multi_dataset\n\ndef make_batch(index_list, batch_size, drop_last): \n if drop_last:\n batches = []\n whole_batch_num = len(index_list)//batch_size\n for _ in range(whole_batch_num):\n batches.append(index_list[batch_size*_:(batch_size*(_+1))])\n else:\n batches = []\n whole_batch_num = math.ceil(len(index_list)/batch_size)\n for _ in range(whole_batch_num):\n batches.append(index_list[batch_size*_:(batch_size*(_+1))])\n return batches \n \ndef batch_generation(dataset,batch_size_2D, batch_size_3D,drop_last=False,shuffle = True, seed = 0):\n \n len_2D = len(dataset.data_whole_2D)\n len_3D = len(dataset.data_whole_3D)\n index_2D = list(range(len_2D))\n index_3D = list(range(len_2D,(len_2D+len_3D)))\n assert len(index_2D) + len(index_3D) == len(dataset.data_whole)\n \n if shuffle: \n # deterministically shuffle based on epoch and seed\n g = torch.Generator()\n g.manual_seed(seed)\n random.shuffle(index_2D)\n random.shuffle(index_3D)\n \n batch_2D = make_batch(index_2D, batch_size_2D, drop_last)\n batch_3D = make_batch(index_3D, batch_size_3D, drop_last)\n \n batch_chunk = batch_2D + batch_3D \n return batch_chunk \n \nclass My_DistributedBatchSampler(Sampler):\n \"\"\" Iterable wrapper that distributes data across multiple workers.\n\n Args:\n iterable (iterable)\n num_replicas (int, optional): Number of processes participating in distributed training.\n rank (int, optional): Rank of the current process within ``num_replicas``.\n\n Example:\n >>> list(DistributedSampler(range(10), num_replicas=2, rank=0))\n [0, 2, 4, 6, 8]\n >>> list(DistributedSampler(range(10), num_replicas=2, rank=1))\n [1, 3, 5, 7, 9]\n \"\"\"\n\n def __init__(self, dataset, num_replicas=None, rank=None, batch_size_2D = 4, batch_size_3D = 1, drop_last = False, shuffle = True, seed: int = 0):\n self.num_replicas = num_replicas\n self.rank = rank\n self.drop_last = drop_last\n self.shuffle = shuffle\n self.dataset = dataset\n self.batch_size_2D = batch_size_2D\n self.batch_size_3D = batch_size_3D\n self.seed = seed \n self.epoch = 0\n \n if num_replicas is None or rank is None: # pragma: no cover\n if not torch.distributed.is_initialized():\n raise RuntimeError('Requires `torch.distributed` to be initialized.')\n\n self.num_replicas = (\n torch.distributed.get_world_size() if num_replicas is None else num_replicas)\n self.rank = torch.distributed.get_rank() if rank is None else rank\n\n indices = batch_generation(self.dataset,self.batch_size_2D,self.batch_size_3D,self.drop_last,self.shuffle)\n if self.rank >= self.num_replicas:\n raise IndexError('`rank` must be smaller than the `num_replicas`.')\n\n if self.drop_last and len(indices) % self.num_replicas != 0: # type: ignore[arg-type]\n # Split to nearest available length that is evenly divisible.\n # This is to ensure each rank receives the same amount of data when\n # using this Sampler.\n self.num_samples = math.ceil(\n (len(indices) - self.num_replicas) / self.num_replicas # type: ignore[arg-type]\n )\n else:\n self.num_samples = math.ceil(len(indices) / self.num_replicas) # type: ignore[arg-type]\n self.total_size = self.num_samples * self.num_replicas\n \n def __iter__(self):\n indices = batch_generation(self.dataset,self.batch_size_2D,self.batch_size_3D,self.drop_last,self.shuffle,self.seed + self.epoch)\n # print(indices)\n if self.shuffle:\n # deterministically shuffle based on epoch and seed\n g = torch.Generator()\n g.manual_seed(self.seed + self.epoch)\n random.shuffle(indices)\n \n if not self.drop_last:\n # add extra samples to make it evenly divisible\n padding_size = self.total_size - len(indices)\n if padding_size <= len(indices):\n indices += indices[:padding_size]\n else:\n indices += (indices * math.ceil(padding_size / len(indices)))[:padding_size]\n else:\n # remove tail of data to make it evenly divisible.\n indices = indices[:self.total_size]\n assert len(indices) == self.total_size\n\n # subsample\n indices = indices[self.rank:self.total_size:self.num_replicas]\n assert len(indices) == self.num_samples\n\n return iter(indices)\n \n def __len__(self):\n return self.num_samples\n \n def set_epoch(self, epoch: int) -> None:\n r\"\"\"\n Set the epoch for this sampler.\n\n When :attr:`shuffle=True`, this ensures all replicas\n use a different random ordering for each epoch. Otherwise, the next iteration of this\n sampler will yield the same ordering.\n\n Args:\n epoch (int): Epoch number.\n \"\"\"\n self.epoch = epoch\n \n\n# print(My_DistributedBatchSampler)\n# Train_dataset = multi_dataset(text_tokenizer = '/mnt/petrelfs/share_data/zhangxiaoman/CODE/RadFM/src/Language_models/tokenizer') \n\n# DDP_sample_0 = list(My_DistributedBatchSampler(dataset= Train_dataset , num_replicas = 32, rank = 0,))\n# DDP_sample_1 = list(My_DistributedBatchSampler(dataset= Train_dataset , num_replicas = 32, rank = 1,))\n\n# for ii in DDP_sample_0:\n# print(ii)\n\n# for ii in DDP_sample_1:\n# print(ii)\n \n"
},
{
"path": "src/output_csv_example/caption_example.csv",
"content": "Question,Ground Truth,Pred,belong_to\n['Could you provide an impression for this chest scan?'],['Impression: No acute cardiopulmonary process.'],['Impression: No pneumothorax.'],['chestxray_caption_dataset']\n\"[\"\"I'm requesting a caption consists of findings and impression to clarify this X-ray image.\"\"]\",['Findings: Left PICC tip is seen terminating in the region of the distal left brachiocephalic vein. Tracheostomy tube is in unchanged standard position. The heart is moderately enlarged. Marked calcification of the aortic knob is again present. Mild pulmonary vascular congestion is similar. Bibasilar streaky airspace opacities are minimally improved. Previously noted left pleural effusion appears to have resolved. No pneumothorax is identified. Percutaneous gastrostomy tube is seen in the left upper quadrant. Impression: 1. Left PICC tip appears to terminate in the distal left brachiocephalic vein. 2. Mild pulmonary vascular congestion. 3. Interval improvement in aeration of the lung bases with residual streaky opacity likely reflective of atelectasis. Interval resolution of the left pleural effusion.'],['Findings: There is a moderate-sized left pleural effusion. Blunting of the right costophrenic angle may be secondary to a small pleural effusion. There is associated atelectasis at the left lung base. No pneumothorax is identified. Heart size is likely top normal. Impression: Moderate-sized left and possible small right pleural effusions.'],['chestxray_caption_dataset']\n['Please write a radiology report consists of findings that explains this X-ray image.'],['Findings: Single AP portable view of the chest. No prior. The lungs are clear of large confluent consolidation. Cardiac silhouette enlarged but could be accentuated by positioning and relatively low inspiratory effort. Calcifications noted at the aortic arch. Degenerative changes noted at the glenohumeral joints bilaterally. Osseous and soft tissue structures otherwise unremarkable. '],['Findings: The heart is mildly enlarged. The mediastinal and hilar contours appear unchanged. There is perihilar fullness and a central widespread diffuse abnormality with indistinct vascularity suggesting mild to moderate pulmonary congestion. There is no definite pleural effusion or pneumothorax. '],['chestxray_caption_dataset']\n['Please caption consists of findings this medical image'],\"['Findings: A hazy opacity is present in the right lung which may represent aspiration, pleural effusion or hemorrhage. Retrocardiac opacity at the left base is unchanged. Moderate cardiomegaly is stable. Slight prominence of the pulmonary vasculature with cephalization and enlarged pulmonary arteries are consistent with mild pulmonary edema. Tracheostomy tube is in place. There are no displaced rib fractures. ']\",['Findings: The endotracheal tube terminates 3.5 cm above the carina. A left IJ central venous catheter terminates at the upper SVC. External pacer wires are demonstrated. Large bilateral pleural effusions and mild central vascular congestion and edema are otherwise unchanged. There is no pneumothorax. '],['chestxray_caption_dataset']\n['Please write an impression for this X-ray scan.'],\"['Impression: Mild residual retrocardiac opacification remains, pneumonia vs. atelectasis.']\",['Impression: 1. Mild pulmonary edema. 2. Low lung volumes.'],['chestxray_caption_dataset']\n['Could you describe what this chest X-ray image shows in a caption consists of findings and impression?'],\"['Findings: Single AP portable chest radiograph is obtained. Tracheostomy tube is present. There is no pneumothorax or pleural effusion. There is a hazy veil-like opacity in the right upper lung zone which may be consolidation, atelectasis or artifact. Heart size appears enlarged; however, this may be technical due to AP view. Bony structures are intact. Impression: Limited study with hazy opacity in the right upper and mid lungs which may be infectious in etiology, atelectasis or artifact.']\",\"['Findings: The endotracheal tube terminates 3.5 cm above the carina. A left IJ central venous catheter terminates at the mid SVC. There is no pneumothorax. There is increased left basilar retrocardiac opacification, likely representing atelectasis. Impression: 1. Moderate left pleural effusion, increased since the prior study. 2. Increased left basilar retrocardiac atelectasis.']\",['chestxray_caption_dataset']\n['Could you provide a caption consists of findings and impression for this X-ray image?'],['Findings: The lungs are clear bilaterally with no areas of focal consolidation. There is no pleural effusion or pneumothorax. Patient is status post CABG. Cardiomegaly is stable. Mediastinal silhouette is within normal limits. Impression: No evidence of pneumonia. Stable cardiomegaly.'],['Findings: The lungs are clear. The cardiomediastinal silhouette is within normal limits. No acute osseous abnormalities. Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Can you offer a descriptive radiology report consists of findings for this chest X-ray scan?'],\"['Findings: The lungs are well expanded and clear. The cardiomediastinal silhouette, hilar contours, and pleural surfaces are normal. No pleural effusion or pneumothorax is present. Sternal wires are intact. ']\",['Findings: The lungs are clear without focal consolidation. No pleural effusion or pneumothorax is seen. The cardiac and mediastinal silhouettes are stable. '],['chestxray_caption_dataset']\n['Please given an impression this chest X-ray image'],['Impression: No acute cardiopulmonary process.'],['Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Please given an impression this chest image'],\"['Impression: No acute cardiopulmonary process. No significant interval change. Additionally, several pulmonary nodules measuring up to 3 mm are not not well appreciated on the current study-CT is more sensitive.']\",['Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Please given an impression this X-ray image'],['Impression: Bilateral lower lobe bronchiectasis with improved right lower medial lung peribronchial consolidation.'],['Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Please write a radiology report consists of findings that explains this X-ray image.'],['Findings: Portable AP chest radiograph demonstrates a stable right pneumothorax. There are several radiodense lesions along the left hemithorax that may represent skin folds. There is no definite left pneumothorax. There is opacification of the left thorax consistent with a moderate left pleural effusion. There is also new engorgement of the pulmonary vasculature in the left lung. There is slight shift of the mediastinum to the right. Bilateral pigtail drains are noted at the costophrenic angles. The heart size is within normal limits. '],['Findings: The lungs are clear. There is no pneumothorax. The heart and mediastinum are within normal limits. Regional bones and soft tissues are unremarkable. '],['chestxray_caption_dataset']\n['Please caption this chest X-ray scan with findings and impression'],\"['Findings: Following pigtail catheter placement in the right lower chest, moderate right pleural effusion has near completely resolved. Moderate-to-large left pleural effusion associated with left lower lung atelectasis and mediastinal shift to the right side is unchanged. There is no pneumothorax. Obscured left mediastinal and the heart borders by pleural effusion limited assessment of the cardiomediastinal silhouette. Impression: Folowing right pigtail catheter placement, moderate right pleural effusion has near completely resolved, whereas large left pleural effusion associated with passive collapse of adjacent lung and mediastinal shift to the right side is persisting. No pneumothorax.']\",['Findings: There is a moderate left pleural effusion with adjacent compressive collapse. The right lung is clear. Cardiomediastinal and hilar contours are normal. No pneumothorax. Impression: Moderate left pleural effusion with adjacent compressive collapse.'],['chestxray_caption_dataset']\n['Please given an impression this X-ray image'],['Impression: 1. New mild-to-moderate left pneumothorax with mild rightward shift of the mediastinum. 2. Stable right pneumothorax. 3. Moderate left pleural effusion.'],['Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Can you offer a descriptive caption consists of findings for this medical image?'],\"['Findings: AP upright and lateral chest radiographs were obtained. Known interstitial lung disease contributes to a bilateral perihilar interstitial abnormality. In addition to the chronic findings there is bilateral ground-glass opacity and interstitial thickening, predominantly radiating from the hila. Cardiomegaly remains moderate. Aortic arch calcifications are unchanged. A right-sided PICC line terminates in the low SVC. A left chest Port-A-Cath terminates in the right atrium. Vertebroplasty changes are stable. ']\",\"['Findings: Lung volumes are low. There is no focal consolidation, pleural effusion or pneumothorax. The aorta is tortuous. There is a hiatal hernia. Clips are present in the right upper quadrant. ']\",['chestxray_caption_dataset']\n['A radiology report consists of findings and impression is needed to explain this chest X-ray scan.'],\"['Findings: New left-sided Port-A-Cath is seen entering the left subclavian and terminating within the right atrium and can be withdrawn 3 cm and still remains within the low SVC. There is stable mild-to-moderate pulmonary edema and stable small bilateral pleural effusions. Again seen are low lung volumes. Heart is stably enlarged. There is no pneumothorax. . Impression: Left-sided Port-A-Cath placement, terminates within the right atrium. Can be withdrawn 3 cm and still remain within the low SVC.']\",['Findings: The lungs are clear. There is no pneumothorax. The heart and mediastinum are within normal limits. Regional bones and soft tissues are unremarkable. Impression: Clear lungs with no evidence of pneumonia.'],['chestxray_caption_dataset']\n['Can you offer an impression for this chest scan?'],\"['Impression: Mild pulmonary edema superimposed on known lung fibrosis. Severe chronic cardiomegaly and pulmonary hypertension. No displaced rib fracture. Multiple vertebroplasties, similar to prior.']\",['Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Please write an impression for this chest scan.'],['Impression: No acute intrathoracic process.'],['Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['A radiology report consists of findings and impression is needed to explain this medical scan.'],\"['Findings: Lung volumes are low. Elevation of the right hemidiaphragm appears similar. Cardiomegaly is again noted. Minimal linear left basilar opacity appears similar and likely represents atelectasis. Of note, evaluation is slightly limited in the absence of lateral view. No pleural effusion or pneumothorax is seen on this single view. No focal consolidation is seen on this single view. Aortic calcifications are again noted. Radiopaque material in the left abdomen may represent previously ingested oral contrast. Impression: Stable frontal chest radiograph. Limited evaluation in the setting of single frontal view; lateral view would be helpful for more thorough evaluation.']\",['Findings: The cardiomediastinal and hilar contours are within normal limits. Increased opacity at the right lung base could reflect atelectasis in the setting of low lung volumes. Aspiration however cannot be excluded. There is no definite pleural effusion or pneumothorax. Impression: Increased opacity at the right lung base could reflect atelectasis in the setting of low lung volumes. Aspiration however cannot be excluded.'],['chestxray_caption_dataset']\n['Could you provide an impression for this chest scan?'],['Impression: No acute cardiopulmonary process.'],['Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n\"[\"\"I'm requesting a radiology report consists of findings and impression to clarify this X-ray image.\"\"]\",['Findings: The nasogastric tube is in adequate position and there is a resolution of the gastric distention. There is still mild bibasilar atelectasis. There are no pneumothorax no pleural effusion. The cardiac and mediastinal contour are unchanged. Impression: The nasogastric tube is in adequate position and there is resolution of the gastric distention.'],['Findings: There is a moderate right pleural effusion with adjacent atelectasis. There is also a small left pleural effusion. There is mild interstitial edema. The cardiac silhouette is mildly enlarged. The aorta is calcified and tortuous. No pneumothorax is seen. Impression: Moderate right and small left pleural effusions with mild interstitial edema.'],['chestxray_caption_dataset']\n['Could you provide an impression for this X-ray scan?'],['Impression: Mild acute congestive heart failure.'],['Impression: 1. Mild pulmonary edema. 2. Moderate cardiomegaly.'],['chestxray_caption_dataset']\n['Could you provide a radiology report consists of findings for this X-ray scan?'],\"['Findings: A left pectoral dual-lead pacer with trans-subclavian lead extending to the right atrium and right ventricle is in unchanged position. There is no pneumothorax or pleural effusion. Hyperexpansion suggests underlying chronic obstructive pulmonary disease. New from prior study, there are multifocal lower and middle lobe parenchymal opacities. Given the provided history and the apparent lack of infectious symptoms, these could represent amiodarone toxicity. The hilar and cardiomediastinal contours are unchanged. There is no pulmonary vascular congestion or pulmonary edema to suggest congestive failure. ']\",['Findings: The lungs are well expanded and clear. The hila and pulmonary vasculature are normal. No pleural abnormalities or pneumothorax. The cardiomediastinal silhouette is normal. No fractures. '],['chestxray_caption_dataset']\n['Please write a caption consists of findings that explains this medical image.'],\"['Findings: The lungs are clear of consolidation, effusion, or pneumothorax. Left chest wall dual lead pacing device is again seen. Moderate cardiomegaly is again noted. Upper thoracic dextroscoliosis is seen. No acute fracture identified based on this nondedicated exam. Surgical clips seen in the upper abdomen. ']\",['Findings: The lungs are hyperinflated. There is no focal consolidation or pneumothorax. Blunting of the left costophrenic sulcus may be due to a small pleural effusion. Scarring in the right mid lung is again seen. '],['chestxray_caption_dataset']\n['A caption consists of findings and impression is needed to explain this chest X-ray scan.'],\"[\"\"Findings: Single portable view of the chest. Low lung volumes are again noted. Chronic changes compatible with patients pulmonary fibrosis are noted. More severely affected areas seen at the bases, left greater than right. Cardiomediastinal silhouette is stable. No acute osseous abnormalities identified. Impression: Findings again compatible with patient's known pulmonary fibrosis without definite superimposed acute process, noting that subtle change would be difficult to detect based on a portable film.\"\"]\",\"['Findings: There is a moderate-sized right pleural effusion. There is mild interstitial edema. Lung volumes are low, exaggerating heart and mediastinal contours; there is likely underlying mild cardiomegaly. The aorta is tortuous. Increased density at the right lung base likely represents atelectasis, but infection cannot be excluded. Impression: Moderate-sized right pleural effusion and mild interstitial edema. Increased density at the right lung base, likely atelectasis, but consolidation cannot be excluded. Follow-up imaging after treatment is recommended.']\",['chestxray_caption_dataset']\n['Could you provide an impression for this chest scan?'],\"['Impression: Right lower lobe pneumonia, which has not cleared, and small right pleural effusion.']\",['Impression: Mild pulmonary edema.'],['chestxray_caption_dataset']\n['Please write a caption consists of findings that explains this chest X-ray scan.'],\"['Findings: The patient is status post left upper lobectomy, with expected persistent left lung volume loss and shift of mediastinal structures. The cardiac, mediastinal, and hilar contours are unchanged, allowing for differences in technique and rotation of the patient. Biapical scarring is again seen. There is no pneumothorax or new consolidation. ']\",\"['Findings: The lungs are clear without focal consolidation, effusion, or edema. Cardiomediastinal silhouette is within normal limits. Small hiatal hernia is noted. ']\",['chestxray_caption_dataset']\n['Could you provide an impression for this chest X-ray image?'],['Impression: Cardiomegaly and venous congestion.'],['Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n\"[\"\"I'm requesting a radiology report consists of findings and impression to clarify this X-ray image.\"\"]\",\"[\"\"Findings: AP single view of the chest has been obtained with patient in sitting semi-upright position. Again noted is status post sternotomy and significant enlargement of the cardiac silhouette. Previously described permanent pacer in left axillary position with two intracavitary electrodes in unchanged location. Unchanged position of left internal jugular approach central venous line terminating in upper portion of SVC. No pneumothorax has developed. Diffuse haze over both lung bases as before obliterating the diaphragmatic contours and indicative of bilateral pleural effusions partially layering posteriorly. The pulmonary venous congestive pattern persists. An intra-aortic balloon pump device is seen to terminate in the descending thoracic aorta about 3 cm below the level of the lower thoracic arch contour. This is unchanged. Impression: No significant interval changes during the last 24 hours interval. The described changes with postoperative status, CHF, pleural effusion and intra-aortic balloon pump device in place is of course compatible with the patient's hypoxia.\"\"]\",['Findings: The right IJ central venous catheter terminates in the mid SVC. Bibasilar subsegmental atelectasis with low lung volumes are present. There is stable elevation of the right hemidiaphragm. Impression: Right IJ terminating in the mid SVC. Bibasilar subsegmental atelectasis with low lung volumes.'],['chestxray_caption_dataset']\n['Could you provide a caption consists of findings and impression for this chest X-ray scan?'],\"['Findings: The heart size is normal. Lung volumes are low. Biapical fibrotic changes with traction bronchiectasis is re- demonstrated. Minimal blunting of the left costophrenic angle suggests a trace left pleural effusion. Streaky bibasilar airspace opacities likely reflect atelectasis. No pneumothorax is identified. Known fracture of the left 11th rib is not clearly delineated on this exam. Clips are seen projecting over the left upper quadrant. No new fractures are seen. There is crowding of the bronchovascular structures but no overt pulmonary edema is demonstrated. Impression: Chronic fibrotic changes within both lung apices. Low lung volumes with probable bibasilar atelectasis, though infection or aspiration cannot be excluded. Small left pleural effusion. Known left 11th rib fracture is not clearly seen on the current exam.']\",['Findings: The lungs are clear. The cardiomediastinal silhouette is within normal limits. No acute osseous abnormalities. Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Can you offer a descriptive caption consists of findings and impression for this X-ray image?'],\"['Findings: Endotracheal tube tip terminates approximately 3.8 cm from the carina. An orogastric tube tip is noted within the distal stomach. Lung volumes are low. Heart size is normal. Mediastinal contours are unremarkable. Crowding of the bronchovascular structures is noted, and mild pulmonary vascular congestion is likely present. Additionally, more focal somewhat linear opacities within both upper lobes appear to be associated with fibrotic changes. No pleural effusion or pneumothorax is identified, although the right costophrenic angle is excluded from the field of view. Diffuse gaseous distention of the bowel loops are noted within the upper abdomen. No acute osseous abnormality seen. Surgical anchors are noted projecting over the right shoulder. Impression: 1. Standard positions of the endotracheal and orogastric tubes. 2. Focal, somewhat linear opacities within both upper lobes which may be due to a chronic interstitial process. Correlation with prior imaging is recommended. Aspiration or infection, however, cannot be completely excluded. 3. Mild pulmonary vascular congestion in the setting of low lung volumes.']\",['Findings: The lungs are clear. The cardiomediastinal silhouette is within normal limits. No acute osseous abnormalities. Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n\"[\"\"I'm requesting a radiology report consists of findings and impression to clarify this medical scan.\"\"]\",['Findings: PA and lateral views of the chest. Bilateral upper lobe scarring is seen with superior retraction of the hila. The lung volumes are relatively low. There is no evidence of superimposed acute process. Cardiomediastinal silhouette is stable. Surgical clips in the upper abdomen again noted. Osseous structures are essentially unremarkable noting probable right glenoid orthopedic hardware. Impression: Bilateral upper lobe scarring unchanged without evidence of superimposed acute process.'],['Findings: The lungs are clear without focal consolidation. No pleural effusion or pneumothorax is seen. The cardiac and mediastinal silhouettes are unremarkable. Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Could you provide an impression for this chest X-ray image?'],['Impression: 1. Increase in moderate left loculated pleural effusion. 2. Worsening of left lung pneumonia.'],['Impression: 1. No evidence of pneumonia. 2. Mild pulmonary edema.'],['chestxray_caption_dataset']\n\"[\"\"I'm requesting a caption consists of findings and impression to clarify this X-ray image.\"\"]\",['Findings: PA and lateral views of the chest were provided. Effusion is seen. No pneumothorax. No signs of pulmonary edema. The heart appears stable in size. The mediastinal contour is unchanged. Bony structures are intact. Anchors are partially imaged at the right glenoid. Impression: Resolving b/l upper lobe pneumonia.'],['Findings: The lungs are clear without focal consolidation. No pleural effusion or pneumothorax is seen. The cardiac and mediastinal silhouettes are unremarkable. Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Can you offer a descriptive caption consists of findings for this chest X-ray image?'],\"['Findings: The cardiac, mediastinal, and hilar contours appear unchanged. Multifocal opacities which persist in the upper lungs with volume loss suggest chronic scarring without definite superimposed disease. Blunting of the left posterior costophrenic sulcus is unchanged, suggesting either trace pleural effusion or pleural thickening. Bony structures are unremarkable. ']\",['Findings: The lungs are clear without focal consolidation. No pleural effusion or pneumothorax is seen. The cardiac and mediastinal silhouettes are unremarkable. '],['chestxray_caption_dataset']\n['Can you offer an impression for this medical scan?'],['Impression: New right upper lobe consolidation worrisome for infection on background chronic scarring.'],['Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Could you describe what this chest X-ray image shows in a radiology report consists of findings?'],\"['Findings: Lung volumes are reduced. The left internal jugular central venous catheter has been removed. The heart size is borderline enlarged, but accentuated due to low inspiratory lung volumes. There is crowding of the bronchovascular structures with probable mild pulmonary vascular congestion. Worsening consolidative opacity in the right upper lung field as well as focal opacities within the left upper and bilateral lower lung fields are concerning for multifocal pneumonia. No pleural effusion or pneumothorax is seen. No acute osseous abnormalities visualized. Clips are demonstrated within the left upper quadrant of the abdomen. ']\",['Findings: There is a large left pleural effusion. The right lung is clear. The cardiomediastinal silhouette is obscured. No pneumothorax is identified. Atherosclerotic calcifications of the aortic arch are noted. '],['chestxray_caption_dataset']\n['Please write a radiology report consists of findings and impression that explains this X-ray image.'],['Findings: Single frontal radiograph of the chest was performed and reveals no acute cardiopulmonary process. The cardiomediastinal and pleural structures are unremarkable. There is scarring in the upper lungs with superior traction of the hila. There is no pleural effusion or pneumothorax. Heart size is normal. Impression: No acute cardiopulmonary process.'],['Findings: The lungs are clear. The cardiomediastinal silhouette is within normal limits. No acute osseous abnormalities. Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['A caption consists of findings and impression is needed to explain this X-ray image.'],['Findings: The heart size is within normal limits. Mediastinal and hilar contours are normal. The previously described resolving right upper lobe pneumonia has improved. There is increasing density over most of the left lung with a small left-sided pleural effusion. There is no pneumothorax. Anchors are present within the right glenoid. Impression: Improving right upper lobe pneumonia and increasing left lung opacity concerning for worsening or new pneumonia.'],['Findings: The lungs are clear. The cardiomediastinal silhouette is within normal limits. No acute osseous abnormalities. Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Can you offer an impression for this medical scan?'],['Impression: No evidence of pneumothorax.'],['Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n['Could you provide an impression for this chest scan?'],\"['Impression: Increased opacity projecting over the right hemi thorax likely due to increased right pleural effusion with overlying atelectasis, underlying infectious process not excluded.']\",['Impression: 1. Mild pulmonary edema. 2. Moderate cardiomegaly. 3. No evidence of pneumonia.'],['chestxray_caption_dataset']\n['A caption consists of findings and impression is needed to explain this X-ray image.'],['Findings: Cardiomediastinal contours are unchanged. The lungs are hyperinflated. There is no pneumothorax. Loculated right pleural effusion has increased. Small left effusion is stable. There are no evident thickening lung abnormality. Degenerative changes in the thoracic spine are again noted Impression: Increase in size in loculated right pleural effusion'],\"['Findings: There is a small right pleural effusion with overlying atelectasis. Right base opacity may be due to combination of pleural effusion and atelectasis, but consolidation due to pneumonia is not excluded. No pneumothorax is seen. The cardiac silhouette is enlarged. Mediastinal contours are stable. The aorta is calcified. No pulmonary edema is seen. Impression: Small right pleural effusion with overlying atelectasis. Right base opacity may be due to combination of pleural effusion and atelectasis but additional consolidation not excluded. Cardiomegaly. No pulmonary edema.']\",['chestxray_caption_dataset']\n['Please write an impression for this chest X-ray image.'],['Impression: Diffusely increased opacity throughout the right lung is related to a chronic loculated right effusion and multifocal opacities worrisome for pneumonia.'],['Impression: No acute cardiopulmonary process.'],['chestxray_caption_dataset']\n\"[\"\"I'm requesting a caption consists of findings to clarify this medical scan.\"\"]\",\"['Findings: There is a persistence of a moderate layering pulmonary effusion on the right. There are no new focal opacities concerning for pneumonia. There is no pneumothorax. The cardiomediastinal and hilar contours are stable, with mild cardiomegaly. Pulmonary vascularity is not increased. ']\",\"['Findings: There is a small right pleural effusion with overlying atelectasis. Right base opacity may be due to combination of pleural effusion and atelectasis, but consolidation due to pneumonia is not excluded. No pneumothorax is seen. The cardiac silhouette is enlarged. Mediastinal contours are stable. The aorta is calcified. No pulmonary edema is seen. ']\",['chestxray_caption_dataset']\n"
},
{
"path": "src/test.py",
"content": "# Import necessary libraries for data processing, modeling, and utilities\nimport tqdm.auto as tqdm\nimport torch.nn.functional as F\nfrom typing import Optional, Dict, Sequence\nfrom typing import List, Optional, Tuple, Union\nimport transformers\nfrom My_Trainer.trainer import Trainer\nfrom dataclasses import dataclass, field\nfrom Dataset.multi_dataset_test import multi_dataset\nfrom Model.RadFM.multimodality_model import MultiLLaMAForCausalLM\nfrom datasampler import My_DistributedBatchSampler\nimport torch\nfrom torch.utils.data import DataLoader \nimport csv\nimport random\nimport numpy as np\n\ndef setup_seed(seed):\n \"\"\"\n Set random seeds for reproducibility across different libraries\n \n Args:\n seed: Integer seed value to use\n \"\"\"\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n np.random.seed(seed)\n random.seed(seed)\n torch.backends.cudnn.deterministic = True\n\n# Set seed for reproducibility\nsetup_seed(20)\n\n\n@dataclass\nclass ModelArguments:\n \"\"\"\n Arguments related to model paths and configuration\n \"\"\"\n lang_encoder_path: Optional[str] = field(default=\"/home/cs/leijiayu/wuchaoyi/book_pretrain/Results/Book_mix_2048_13B_full/checkpoint-45800\")\n tokenizer_path: str = field(default='/home/cs/leijiayu/wuchaoyi/Finetune_LLAMA/LLAMA_Model/tokenizer', metadata={\"help\": \"Path to the tokenizer data.\"}) \n #vision_encoder_path: str = field(default='/home/cs/leijiayu/wuchaoyi/multi_modal/src/PMC-CLIP/checkpoint.pt', metadata={\"help\": \"Path to the vision_encoder.\"}) \n \n\n@dataclass\nclass DataArguments:\n \"\"\"\n Arguments related to dataset configuration and testing modes\n \"\"\"\n Mode: Optional[str] = field(default=\"Train\")\n test_split: Optional[str] = field(default=\"open\")\n \n@dataclass\nclass TrainingArguments(transformers.TrainingArguments):\n \"\"\"\n Custom training arguments extending HuggingFace's TrainingArguments\n with additional parameters for multimodal training\n \"\"\"\n remove_unused_columns: bool = field(default = False)\n batch_size_2D: int = field(default = 4) # Batch size for 2D data\n batch_size_3D: int = field(default = 1) # Batch size for 3D data\n output_dir: Optional[str] = field(default=\"/home/cs/leijiayu/wuchaoyi/multi_modal/src/Results/BLIP_overfit/\")\n cache_dir: Optional[str] = field(default=None)\n optim: str = field(default=\"adamw_torch\")\n\n\n@dataclass\nclass DataCollator(object):\n \"\"\"\n Data collator for preparing batches of multimodal inputs for the model\n \"\"\"\n\n def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:\n # Extract different components from the input instances\n vision_xs, lang_xs, attention_masks, labels = tuple(\n [instance[key] for instance in instances] \n for key in ('vision_x','lang_x', 'attention_mask', 'labels')\n )\n \n # Stack language tensors along batch dimension\n lang_xs = torch.cat([_.unsqueeze(0) for _ in lang_xs], dim=0)\n attention_masks = torch.cat([_.unsqueeze(0) for _ in attention_masks], dim=0)\n labels = torch.cat([_.unsqueeze(0) for _ in labels], dim=0)\n \n # Set target dimensions for resizing vision inputs\n target_H = 512\n target_W = 512\n target_D = 4\n MAX_D = 0\n \n # Reduce resolution for single samples to save memory\n if len(vision_xs) == 1:\n target_H = 256\n target_W = 256\n \n # Define possible depth values for 3D data \n D_list = list(range(4,65,4))\n # Adjust depth values for large inputs\n if len(vision_xs) == 1:\n if vision_xs[0].shape[0] > 6:\n D_list = list(range(4,33,4))\n \n # Find maximum depth in current batch\n for ii in vision_xs:\n try:\n D = ii.shape[-1]\n if D > MAX_D:\n MAX_D = D\n except:\n continue\n \n # Select closest target depth from available options\n for temp_D in D_list:\n if abs(temp_D - MAX_D) < abs(target_D - MAX_D):\n target_D = temp_D\n\n # Resize all vision inputs to target dimensions\n vision_xs = [torch.nn.functional.interpolate(s, size=(target_H, target_W, target_D)) for s in vision_xs]\n \n # Pad sequence for variable-length vision inputs\n vision_xs = torch.nn.utils.rnn.pad_sequence(\n vision_xs, batch_first=True, padding_value=0\n )\n print(vision_xs.shape)\n \n # Return collated batch\n return dict(\n lang_x=lang_xs,\n vision_x=vision_xs,\n attention_mask=attention_masks,\n labels=labels,\n )\n \ndef main():\n \"\"\"\n Main function to set up and run the inference process\n \"\"\"\n # Parse command-line arguments\n parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n \n # Set custom data sampler\n training_args.data_sampler = My_DistributedBatchSampler\n \n print(\"Setup Data\")\n # Initialize test dataset with specified split\n Test_dataset = multi_dataset(text_tokenizer=model_args.tokenizer_path, test_split=data_args.test_split)\n \n # Configure DataLoader for test dataset\n Test_dataloader = DataLoader(\n Test_dataset,\n batch_size=1,\n num_workers=1,\n pin_memory=True,\n sampler=None,\n shuffle=True,\n collate_fn=None,\n drop_last=False,\n ) \n \n print(\"Setup Model\")\n # Initialize the multimodal model\n model = MultiLLaMAForCausalLM(\n lang_model_path=model_args.lang_encoder_path,\n )\n \n # Load pre-trained model checkpoint\n ckpt = torch.load('/gpfs/home/cs/leijiayu/wuchaoyi/wangyingjie/src/Results/backup/checkpoint-17600/pytorch_model.bin', map_location='cpu')\n # ckpt.pop('embedding_layer.figure_token_weight')\n model.load_state_dict(ckpt, strict=False)\n model = model.to('cuda')\n model.eval() # Set model to evaluation mode\n \n # Create output CSV file for results\n with open('output_whole_2_epoch' + data_args.test_split + '.csv', mode='w') as outfile:\n writer = csv.writer(outfile)\n writer.writerow([\"Question\", \"Ground Truth\", \"Pred\", 'belong_to'])\n cc = 0\n \n # Process each sample in the test dataset\n for sample in tqdm.tqdm(Test_dataloader):\n question = sample[\"question\"]\n belong_to = sample['belong_to']\n # img_pp = sample['img_path']\n \n # Tokenize the question text\n lang_x = Test_dataset.text_tokenizer(\n question, max_length=2048, truncation=True, return_tensors=\"pt\"\n )['input_ids'].to('cuda')\n \n # Get vision input\n vision_x = sample[\"vision_x\"].to('cuda')\n answer = sample['answer']\n \n try:\n # Generate text based on text and vision inputs\n generation = model.generate(lang_x, vision_x)\n generated_texts = Test_dataset.text_tokenizer.batch_decode(generation, skip_special_tokens=True) \n \n # Write results to CSV\n writer.writerow([question, answer, generated_texts, belong_to])\n cc = cc + 1\n # if cc >= 10000:\n # break\n except:\n continue\n\nif __name__ == \"__main__\":\n main()"
},
{
"path": "src/train.py",
"content": "# Import necessary libraries\nimport tqdm.auto as tqdm\nimport torch.nn.functional as F\nfrom typing import Optional, Dict, Sequence\nfrom typing import List, Optional, Tuple, Union\nimport transformers\nfrom My_Trainer.trainer import Trainer\nfrom dataclasses import dataclass, field\nfrom Dataset.multi_dataset import multi_dataset\nfrom Model.RadFM.multimodality_model import MultiLLaMAForCausalLM\nfrom datasampler import My_DistributedBatchSampler\nfrom datasets import load_metric\nfrom Dataset.multi_dataset_test_for_close import multi_dataset_close\nimport numpy as np\nimport torch\n\n\ndef compute_metrics(eval_preds):\n \"\"\"\n Compute evaluation metrics from prediction outputs.\n Returns the mean accuracy across all predictions.\n \n Args:\n eval_preds: Prediction outputs from the model\n \n Returns:\n Dictionary containing accuracy metric\n \"\"\"\n # metric = load_metric(\"glue\", \"mrpc\")\n ACCs = eval_preds.predictions\n # print(ACCs)\n return {\"accuracy\": np.mean(ACCs, axis=-1)}\n\n@dataclass\nclass ModelArguments:\n \"\"\"\n Arguments pertaining to which model/config/tokenizer we are going to fine-tune.\n \"\"\"\n lang_encoder_path: Optional[str] = field(default=\"/home/cs/leijiayu/wuchaoyi/book_pretrain/Results/Book_mix_2048_13B_full/checkpoint-45800\")\n tokenizer_path: str = field(default='/home/cs/leijiayu/wuchaoyi/Finetune_LLAMA/LLAMA_Model/tokenizer', \n metadata={\"help\": \"Path to the tokenizer data.\"}) \n \n \n\n@dataclass\nclass DataArguments:\n \"\"\"\n Arguments pertaining to data processing mode.\n \"\"\"\n Mode: Optional[str] = field(default=\"Train\")\n \n@dataclass\nclass TrainingArguments(transformers.TrainingArguments):\n \"\"\"\n Custom training arguments extending the HuggingFace TrainingArguments class.\n Includes additional parameters specific to this multimodal training setup.\n \"\"\"\n remove_unused_columns: bool = field(default=False)\n batch_size_2D: int = field(default=4) # Batch size for 2D data\n batch_size_3D: int = field(default=1) # Batch size for 3D data\n output_dir: Optional[str] = field(default=\"/home/cs/leijiayu/wuchaoyi/multi_modal/src/Results/BLIP_overfit/\")\n cache_dir: Optional[str] = field(default=None)\n optim: str = field(default=\"adamw_torch\")\n\n\n@dataclass\nclass DataCollator(object):\n \"\"\"\n Data collator that handles batching of multimodal inputs.\n Processes vision and language inputs, handles padding, and resizes vision inputs.\n \"\"\"\n\n def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:\n # Extract different data components from instances\n vision_xs, lang_xs, attention_masks, labels, loss_reweight, key_words_query = tuple(\n [instance[key] for instance in instances] \n for key in ('vision_x', 'lang_x', 'attention_mask', 'labels', 'loss_reweight', 'key_words_query')\n )\n \n # Stack language tensors along batch dimension\n lang_xs = torch.cat([_.unsqueeze(0) for _ in lang_xs], dim=0)\n attention_masks = torch.cat([_.unsqueeze(0) for _ in attention_masks], dim=0)\n labels = torch.cat([_.unsqueeze(0) for _ in labels], dim=0)\n loss_reweight = torch.cat([_.unsqueeze(0) for _ in loss_reweight], dim=0)\n \n # Set target dimensions for vision input resizing\n target_H = 512\n target_W = 512\n target_D = 4\n MAX_D = 0\n \n # Define possible depth values for 3D data\n D_list = list(range(4, 65, 4))\n # Adjust depth range for larger inputs\n if len(vision_xs) == 1:\n if vision_xs[0].shape[0] > 6:\n D_list = list(range(4, 33, 4))\n \n # Find maximum depth in current batch\n for ii in vision_xs:\n try:\n D = ii.shape[-1]\n if D > MAX_D:\n MAX_D = D\n except:\n continue\n \n # Select closest target depth from available options\n for temp_D in D_list:\n if abs(temp_D - MAX_D) < abs(target_D - MAX_D):\n target_D = temp_D\n \n # Reduce image dimensions for larger depth inputs with small batch size\n if len(vision_xs) == 1 and target_D > 4:\n target_H = 256\n target_W = 256\n \n # Resize all vision inputs to target dimensions\n vision_xs = [torch.nn.functional.interpolate(s, size=(target_H, target_W, target_D)) for s in vision_xs]\n \n # Pad sequence for variable-length vision inputs\n vision_xs = torch.nn.utils.rnn.pad_sequence(\n vision_xs, batch_first=True, padding_value=0\n )\n print(vision_xs.shape, vision_xs.dtype)\n \n # Return collated batch\n return dict(\n lang_x=lang_xs,\n vision_x=vision_xs,\n attention_mask=attention_masks,\n labels=labels,\n loss_reweight=loss_reweight,\n key_words_query=key_words_query\n )\n \ndef main():\n \"\"\"\n Main function to set up and run the training process.\n Parses arguments, initializes datasets, model, and trainer.\n \"\"\"\n # Parse command-line arguments\n parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n \n # Set custom data sampler\n training_args.data_sampler = My_DistributedBatchSampler\n \n print(\"Setup Data\")\n # Initialize training and evaluation datasets\n Train_dataset = multi_dataset(text_tokenizer=model_args.tokenizer_path)\n Eval_dataset = multi_dataset_close(text_tokenizer=model_args.tokenizer_path)\n \n print(\"Setup Model\")\n # Initialize the multimodal model\n model = MultiLLaMAForCausalLM(\n lang_model_path=model_args.lang_encoder_path,\n )\n \n # Setup trainer with model, datasets, and configurations\n trainer = Trainer(\n model=model, \n train_dataset=Train_dataset, \n eval_dataset=Eval_dataset,\n args=training_args,\n data_collator=DataCollator(),\n compute_metrics=compute_metrics\n )\n\n # Start training\n trainer.train()\n # Save training state\n trainer.save_state()\n \nif __name__ == \"__main__\":\n main()"
}
]