├── AlexNet ├── AlexNetDeploy.py ├── finetune_with_AlexNet.py └── read_AlexNet_structure.py ├── LeNet5 ├── data │ └── mnist │ │ ├── processed │ │ ├── test.pt │ │ └── training.pt │ │ └── raw │ │ ├── t10k-images-idx3-ubyte │ │ ├── t10k-labels-idx1-ubyte │ │ ├── train-images-idx3-ubyte │ │ └── train-labels-idx1-ubyte ├── lenet.py ├── mnist_deploy.py └── run.py ├── Self_DataLoader ├── load_data_by_folder.py └── load_data_by_txt.py ├── TransferLearning ├── Finetune_with_different_lr.py └── finetune_Fc_Layer.py ├── cancel_val_grad ├── cancel_val_grad_method_function.py └── cancel_val_grad_method_together.py ├── data ├── hymenoptera_data │ ├── train │ │ ├── ants │ │ │ ├── 0013035.jpg │ │ │ ├── 1030023514_aad5c608f9.jpg │ │ │ ├── 1095476100_3906d8afde.jpg │ │ │ ├── 1099452230_d1949d3250.jpg │ │ │ ├── 116570827_e9c126745d.jpg │ │ │ ├── 1225872729_6f0856588f.jpg │ │ │ ├── 1262877379_64fcada201.jpg │ │ │ ├── 1269756697_0bce92cdab.jpg │ │ │ ├── 1286984635_5119e80de1.jpg │ │ │ ├── 132478121_2a430adea2.jpg │ │ │ ├── 1360291657_dc248c5eea.jpg │ │ │ ├── 1368913450_e146e2fb6d.jpg │ │ │ ├── 1473187633_63ccaacea6.jpg │ │ │ ├── 148715752_302c84f5a4.jpg │ │ │ ├── 1489674356_09d48dde0a.jpg │ │ │ ├── 149244013_c529578289.jpg │ │ │ ├── 150801003_3390b73135.jpg │ │ │ ├── 150801171_cd86f17ed8.jpg │ │ │ ├── 154124431_65460430f2.jpg │ │ │ ├── 162603798_40b51f1654.jpg │ │ │ ├── 1660097129_384bf54490.jpg │ │ │ ├── 167890289_dd5ba923f3.jpg │ │ │ ├── 1693954099_46d4c20605.jpg │ │ │ ├── 175998972.jpg │ │ │ ├── 178538489_bec7649292.jpg │ │ │ ├── 1804095607_0341701e1c.jpg │ │ │ ├── 1808777855_2a895621d7.jpg │ │ │ ├── 188552436_605cc9b36b.jpg │ │ │ ├── 1917341202_d00a7f9af5.jpg │ │ │ ├── 1924473702_daa9aacdbe.jpg │ │ │ ├── 196057951_63bf063b92.jpg │ │ │ ├── 196757565_326437f5fe.jpg │ │ │ ├── 201558278_fe4caecc76.jpg │ │ │ ├── 201790779_527f4c0168.jpg │ │ │ ├── 2019439677_2db655d361.jpg │ │ │ ├── 207947948_3ab29d7207.jpg │ │ │ ├── 20935278_9190345f6b.jpg │ │ │ ├── 224655713_3956f7d39a.jpg │ │ │ ├── 2265824718_2c96f485da.jpg │ │ │ ├── 2265825502_fff99cfd2d.jpg │ │ │ ├── 226951206_d6bf946504.jpg │ │ │ ├── 2278278459_6b99605e50.jpg │ │ │ ├── 2288450226_a6e96e8fdf.jpg │ │ │ ├── 2288481644_83ff7e4572.jpg │ │ │ ├── 2292213964_ca51ce4bef.jpg │ │ │ ├── 24335309_c5ea483bb8.jpg │ │ │ ├── 245647475_9523dfd13e.jpg │ │ │ ├── 255434217_1b2b3fe0a4.jpg │ │ │ ├── 258217966_d9d90d18d3.jpg │ │ │ ├── 275429470_b2d7d9290b.jpg │ │ │ ├── 28847243_e79fe052cd.jpg │ │ │ ├── 318052216_84dff3f98a.jpg │ │ │ ├── 334167043_cbd1adaeb9.jpg │ │ │ ├── 339670531_94b75ae47a.jpg │ │ │ ├── 342438950_a3da61deab.jpg │ │ │ ├── 36439863_0bec9f554f.jpg │ │ │ ├── 374435068_7eee412ec4.jpg │ │ │ ├── 382971067_0bfd33afe0.jpg │ │ │ ├── 384191229_5779cf591b.jpg │ │ │ ├── 386190770_672743c9a7.jpg │ │ │ ├── 392382602_1b7bed32fa.jpg │ │ │ ├── 403746349_71384f5b58.jpg │ │ │ ├── 408393566_b5b694119b.jpg │ │ │ ├── 424119020_6d57481dab.jpg │ │ │ ├── 424873399_47658a91fb.jpg │ │ │ ├── 450057712_771b3bfc91.jpg │ │ │ ├── 45472593_bfd624f8dc.jpg │ │ │ ├── 459694881_ac657d3187.jpg │ │ │ ├── 460372577_f2f6a8c9fc.jpg │ │ │ ├── 460874319_0a45ab4d05.jpg │ │ │ ├── 466430434_4000737de9.jpg │ │ │ ├── 470127037_513711fd21.jpg │ │ │ ├── 474806473_ca6caab245.jpg │ │ │ ├── 475961153_b8c13fd405.jpg │ │ │ ├── 484293231_e53cfc0c89.jpg │ │ │ ├── 49375974_e28ba6f17e.jpg │ │ │ ├── 506249802_207cd979b4.jpg │ │ │ ├── 506249836_717b73f540.jpg │ │ │ ├── 512164029_c0a66b8498.jpg │ │ │ ├── 512863248_43c8ce579b.jpg │ │ │ ├── 518773929_734dbc5ff4.jpg │ │ │ ├── 522163566_fec115ca66.jpg │ │ │ ├── 522415432_2218f34bf8.jpg │ │ │ ├── 531979952_bde12b3bc0.jpg │ │ │ ├── 533848102_70a85ad6dd.jpg │ │ │ ├── 535522953_308353a07c.jpg │ │ │ ├── 540889389_48bb588b21.jpg │ │ │ ├── 541630764_dbd285d63c.jpg │ │ │ ├── 543417860_b14237f569.jpg │ │ │ ├── 560966032_988f4d7bc4.jpg │ │ │ ├── 5650366_e22b7e1065.jpg │ │ │ ├── 6240329_72c01e663e.jpg │ │ │ ├── 6240338_93729615ec.jpg │ │ │ ├── 649026570_e58656104b.jpg │ │ │ ├── 662541407_ff8db781e7.jpg │ │ │ ├── 67270775_e9fdf77e9d.jpg │ │ │ ├── 6743948_2b8c096dda.jpg │ │ │ ├── 684133190_35b62c0c1d.jpg │ │ │ ├── 69639610_95e0de17aa.jpg │ │ │ ├── 707895295_009cf23188.jpg │ │ │ ├── 7759525_1363d24e88.jpg │ │ │ ├── 795000156_a9900a4a71.jpg │ │ │ ├── 822537660_caf4ba5514.jpg │ │ │ ├── 82852639_52b7f7f5e3.jpg │ │ │ ├── 841049277_b28e58ad05.jpg │ │ │ ├── 886401651_f878e888cd.jpg │ │ │ ├── 892108839_f1aad4ca46.jpg │ │ │ ├── 938946700_ca1c669085.jpg │ │ │ ├── 957233405_25c1d1187b.jpg │ │ │ ├── 9715481_b3cb4114ff.jpg │ │ │ ├── 998118368_6ac1d91f81.jpg │ │ │ ├── Ant_1.jpg │ │ │ ├── MehdiabadiAnt2_600.jpg │ │ │ ├── Nepenthes_rafflesiana_ant.jpg │ │ │ ├── VietnameseAntMimicSpider.jpg │ │ │ ├── ant photos.jpg │ │ │ ├── army-ants-red-picture.jpg │ │ │ ├── formica.jpeg │ │ │ ├── hormiga_co_por.jpg │ │ │ ├── imageNotFound.gif │ │ │ ├── kurokusa.jpg │ │ │ ├── swiss-army-ant.jpg │ │ │ ├── termite-vs-ant.jpg │ │ │ └── trap-jaw-ant-insect-bg.jpg │ │ └── bees │ │ │ ├── 1092977343_cb42b38d62.jpg │ │ │ ├── 1093831624_fb5fbe2308.jpg │ │ │ ├── 1097045929_1753d1c765.jpg │ │ │ ├── 1232245714_f862fbe385.jpg │ │ │ ├── 129236073_0985e91c7d.jpg │ │ │ ├── 1295655112_7813f37d21.jpg │ │ │ ├── 132511197_0b86ad0fff.jpg │ │ │ ├── 132826773_dbbcb117b9.jpg │ │ │ ├── 150013791_969d9a968b.jpg │ │ │ ├── 1508176360_2972117c9d.jpg │ │ │ ├── 154600396_53e1252e52.jpg │ │ │ ├── 16838648_415acd9e3f.jpg │ │ │ ├── 1691282715_0addfdf5e8.jpg │ │ │ ├── 17209602_fe5a5a746f.jpg │ │ │ ├── 174142798_e5ad6d76e0.jpg │ │ │ ├── 1799726602_8580867f71.jpg │ │ │ ├── 1807583459_4fe92b3133.jpg │ │ │ ├── 196430254_46bd129ae7.jpg │ │ │ ├── 196658222_3fffd79c67.jpg │ │ │ ├── 198508668_97d818b6c4.jpg │ │ │ ├── 2031225713_50ed499635.jpg │ │ │ ├── 2037437624_2d7bce461f.jpg │ │ │ ├── 2053200300_8911ef438a.jpg │ │ │ ├── 205835650_e6f2614bee.jpg │ │ │ ├── 208702903_42fb4d9748.jpg │ │ │ ├── 21399619_3e61e5bb6f.jpg │ │ │ ├── 2227611847_ec72d40403.jpg │ │ │ ├── 2321139806_d73d899e66.jpg │ │ │ ├── 2330918208_8074770c20.jpg │ │ │ ├── 2345177635_caf07159b3.jpg │ │ │ ├── 2358061370_9daabbd9ac.jpg │ │ │ ├── 2364597044_3c3e3fc391.jpg │ │ │ ├── 2384149906_2cd8b0b699.jpg │ │ │ ├── 2397446847_04ef3cd3e1.jpg │ │ │ ├── 2405441001_b06c36fa72.jpg │ │ │ ├── 2445215254_51698ff797.jpg │ │ │ ├── 2452236943_255bfd9e58.jpg │ │ │ ├── 2467959963_a7831e9ff0.jpg │ │ │ ├── 2470492904_837e97800d.jpg │ │ │ ├── 2477324698_3d4b1b1cab.jpg │ │ │ ├── 2477349551_e75c97cf4d.jpg │ │ │ ├── 2486729079_62df0920be.jpg │ │ │ ├── 2486746709_c43cec0e42.jpg │ │ │ ├── 2493379287_4100e1dacc.jpg │ │ │ ├── 2495722465_879acf9d85.jpg │ │ │ ├── 2528444139_fa728b0f5b.jpg │ │ │ ├── 2538361678_9da84b77e3.jpg │ │ │ ├── 2551813042_8a070aeb2b.jpg │ │ │ ├── 2580598377_a4caecdb54.jpg │ │ │ ├── 2601176055_8464e6aa71.jpg │ │ │ ├── 2610833167_79bf0bcae5.jpg │ │ │ ├── 2610838525_fe8e3cae47.jpg │ │ │ ├── 2617161745_fa3ebe85b4.jpg │ │ │ ├── 2625499656_e3415e374d.jpg │ │ │ ├── 2634617358_f32fd16bea.jpg │ │ │ ├── 2638074627_6b3ae746a0.jpg │ │ │ ├── 2645107662_b73a8595cc.jpg │ │ │ ├── 2651621464_a2fa8722eb.jpg │ │ │ ├── 2652877533_a564830cbf.jpg │ │ │ ├── 266644509_d30bb16a1b.jpg │ │ │ ├── 2683605182_9d2a0c66cf.jpg │ │ │ ├── 2704348794_eb5d5178c2.jpg │ │ │ ├── 2707440199_cd170bd512.jpg │ │ │ ├── 2710368626_cb42882dc8.jpg │ │ │ ├── 2722592222_258d473e17.jpg │ │ │ ├── 2728759455_ce9bb8cd7a.jpg │ │ │ ├── 2756397428_1d82a08807.jpg │ │ │ ├── 2765347790_da6cf6cb40.jpg │ │ │ ├── 2781170484_5d61835d63.jpg │ │ │ ├── 279113587_b4843db199.jpg │ │ │ ├── 2792000093_e8ae0718cf.jpg │ │ │ ├── 2801728106_833798c909.jpg │ │ │ ├── 2822388965_f6dca2a275.jpg │ │ │ ├── 2861002136_52c7c6f708.jpg │ │ │ ├── 2908916142_a7ac8b57a8.jpg │ │ │ ├── 29494643_e3410f0d37.jpg │ │ │ ├── 2959730355_416a18c63c.jpg │ │ │ ├── 2962405283_22718d9617.jpg │ │ │ ├── 3006264892_30e9cced70.jpg │ │ │ ├── 3030189811_01d095b793.jpg │ │ │ ├── 3030772428_8578335616.jpg │ │ │ ├── 3044402684_3853071a87.jpg │ │ │ ├── 3074585407_9854eb3153.jpg │ │ │ ├── 3079610310_ac2d0ae7bc.jpg │ │ │ ├── 3090975720_71f12e6de4.jpg │ │ │ ├── 3100226504_c0d4f1e3f1.jpg │ │ │ ├── 342758693_c56b89b6b6.jpg │ │ │ ├── 354167719_22dca13752.jpg │ │ │ ├── 359928878_b3b418c728.jpg │ │ │ ├── 365759866_b15700c59b.jpg │ │ │ ├── 36900412_92b81831ad.jpg │ │ │ ├── 39672681_1302d204d1.jpg │ │ │ ├── 39747887_42df2855ee.jpg │ │ │ ├── 421515404_e87569fd8b.jpg │ │ │ ├── 444532809_9e931e2279.jpg │ │ │ ├── 446296270_d9e8b93ecf.jpg │ │ │ ├── 452462677_7be43af8ff.jpg │ │ │ ├── 452462695_40a4e5b559.jpg │ │ │ ├── 457457145_5f86eb7e9c.jpg │ │ │ ├── 465133211_80e0c27f60.jpg │ │ │ ├── 469333327_358ba8fe8a.jpg │ │ │ ├── 472288710_2abee16fa0.jpg │ │ │ ├── 473618094_8ffdcab215.jpg │ │ │ ├── 476347960_52edd72b06.jpg │ │ │ ├── 478701318_bbd5e557b8.jpg │ │ │ ├── 507288830_f46e8d4cb2.jpg │ │ │ ├── 509247772_2db2d01374.jpg │ │ │ ├── 513545352_fd3e7c7c5d.jpg │ │ │ ├── 522104315_5d3cb2758e.jpg │ │ │ ├── 537309131_532bfa59ea.jpg │ │ │ ├── 586041248_3032e277a9.jpg │ │ │ ├── 760526046_547e8b381f.jpg │ │ │ ├── 760568592_45a52c847f.jpg │ │ │ ├── 774440991_63a4aa0cbe.jpg │ │ │ ├── 85112639_6e860b0469.jpg │ │ │ ├── 873076652_eb098dab2d.jpg │ │ │ ├── 90179376_abc234e5f4.jpg │ │ │ ├── 92663402_37f379e57a.jpg │ │ │ ├── 95238259_98470c5b10.jpg │ │ │ ├── 969455125_58c797ef17.jpg │ │ │ └── 98391118_bdb1e80cce.jpg │ └── val │ │ ├── ants │ │ ├── 10308379_1b6c72e180.jpg │ │ ├── 1053149811_f62a3410d3.jpg │ │ ├── 1073564163_225a64f170.jpg │ │ ├── 1119630822_cd325ea21a.jpg │ │ ├── 1124525276_816a07c17f.jpg │ │ ├── 11381045_b352a47d8c.jpg │ │ ├── 119785936_dd428e40c3.jpg │ │ ├── 1247887232_edcb61246c.jpg │ │ ├── 1262751255_c56c042b7b.jpg │ │ ├── 1337725712_2eb53cd742.jpg │ │ ├── 1358854066_5ad8015f7f.jpg │ │ ├── 1440002809_b268d9a66a.jpg │ │ ├── 147542264_79506478c2.jpg │ │ ├── 152286280_411648ec27.jpg │ │ ├── 153320619_2aeb5fa0ee.jpg │ │ ├── 153783656_85f9c3ac70.jpg │ │ ├── 157401988_d0564a9d02.jpg │ │ ├── 159515240_d5981e20d1.jpg │ │ ├── 161076144_124db762d6.jpg │ │ ├── 161292361_c16e0bf57a.jpg │ │ ├── 170652283_ecdaff5d1a.jpg │ │ ├── 17081114_79b9a27724.jpg │ │ ├── 172772109_d0a8e15fb0.jpg │ │ ├── 1743840368_b5ccda82b7.jpg │ │ ├── 181942028_961261ef48.jpg │ │ ├── 183260961_64ab754c97.jpg │ │ ├── 2039585088_c6f47c592e.jpg │ │ ├── 205398178_c395c5e460.jpg │ │ ├── 208072188_f293096296.jpg │ │ ├── 209615353_eeb38ba204.jpg │ │ ├── 2104709400_8831b4fc6f.jpg │ │ ├── 212100470_b485e7b7b9.jpg │ │ ├── 2127908701_d49dc83c97.jpg │ │ ├── 2191997003_379df31291.jpg │ │ ├── 2211974567_ee4606b493.jpg │ │ ├── 2219621907_47bc7cc6b0.jpg │ │ ├── 2238242353_52c82441df.jpg │ │ ├── 2255445811_dabcdf7258.jpg │ │ ├── 239161491_86ac23b0a3.jpg │ │ ├── 263615709_cfb28f6b8e.jpg │ │ ├── 308196310_1db5ffa01b.jpg │ │ ├── 319494379_648fb5a1c6.jpg │ │ ├── 35558229_1fa4608a7a.jpg │ │ ├── 412436937_4c2378efc2.jpg │ │ ├── 436944325_d4925a38c7.jpg │ │ ├── 445356866_6cb3289067.jpg │ │ ├── 459442412_412fecf3fe.jpg │ │ ├── 470127071_8b8ee2bd74.jpg │ │ ├── 477437164_bc3e6e594a.jpg │ │ ├── 488272201_c5aa281348.jpg │ │ ├── 502717153_3e4865621a.jpg │ │ ├── 518746016_bcc28f8b5b.jpg │ │ ├── 540543309_ddbb193ee5.jpg │ │ ├── 562589509_7e55469b97.jpg │ │ ├── 57264437_a19006872f.jpg │ │ ├── 573151833_ebbc274b77.jpg │ │ ├── 649407494_9b6bc4949f.jpg │ │ ├── 751649788_78dd7d16ce.jpg │ │ ├── 768870506_8f115d3d37.jpg │ │ ├── 800px-Meat_eater_ant_qeen_excavating_hole.jpg │ │ ├── 8124241_36b290d372.jpg │ │ ├── 8398478_50ef10c47a.jpg │ │ ├── 854534770_31f6156383.jpg │ │ ├── 892676922_4ab37dce07.jpg │ │ ├── 94999827_36895faade.jpg │ │ ├── Ant-1818.jpg │ │ ├── F.pergan.28(f).jpg │ │ ├── Hormiga.jpg │ │ ├── ants-devouring-remains-of-large-dead-insect-on-red-tile-in-Stellenbosch-South-Africa-closeup-1-DHD.jpg │ │ └── desert_ant.jpg │ │ └── bees │ │ ├── 1032546534_06907fe3b3.jpg │ │ ├── 10870992_eebeeb3a12.jpg │ │ ├── 1181173278_23c36fac71.jpg │ │ ├── 1297972485_33266a18d9.jpg │ │ ├── 1328423762_f7a88a8451.jpg │ │ ├── 1355974687_1341c1face.jpg │ │ ├── 144098310_a4176fd54d.jpg │ │ ├── 1486120850_490388f84b.jpg │ │ ├── 149973093_da3c446268.jpg │ │ ├── 151594775_ee7dc17b60.jpg │ │ ├── 151603988_2c6f7d14c7.jpg │ │ ├── 1519368889_4270261ee3.jpg │ │ ├── 152789693_220b003452.jpg │ │ ├── 177677657_a38c97e572.jpg │ │ ├── 1799729694_0c40101071.jpg │ │ ├── 181171681_c5a1a82ded.jpg │ │ ├── 187130242_4593a4c610.jpg │ │ ├── 203868383_0fcbb48278.jpg │ │ ├── 2060668999_e11edb10d0.jpg │ │ ├── 2086294791_6f3789d8a6.jpg │ │ ├── 2103637821_8d26ee6b90.jpg │ │ ├── 2104135106_a65eede1de.jpg │ │ ├── 215512424_687e1e0821.jpg │ │ ├── 2173503984_9c6aaaa7e2.jpg │ │ ├── 220376539_20567395d8.jpg │ │ ├── 224841383_d050f5f510.jpg │ │ ├── 2321144482_f3785ba7b2.jpg │ │ ├── 238161922_55fa9a76ae.jpg │ │ ├── 2407809945_fb525ef54d.jpg │ │ ├── 2415414155_1916f03b42.jpg │ │ ├── 2438480600_40a1249879.jpg │ │ ├── 2444778727_4b781ac424.jpg │ │ ├── 2457841282_7867f16639.jpg │ │ ├── 2470492902_3572c90f75.jpg │ │ ├── 2478216347_535c8fe6d7.jpg │ │ ├── 2501530886_e20952b97d.jpg │ │ ├── 2506114833_90a41c5267.jpg │ │ ├── 2509402554_31821cb0b6.jpg │ │ ├── 2525379273_dcb26a516d.jpg │ │ ├── 26589803_5ba7000313.jpg │ │ ├── 2668391343_45e272cd07.jpg │ │ ├── 2670536155_c170f49cd0.jpg │ │ ├── 2685605303_9eed79d59d.jpg │ │ ├── 2702408468_d9ed795f4f.jpg │ │ ├── 2709775832_85b4b50a57.jpg │ │ ├── 2717418782_bd83307d9f.jpg │ │ ├── 272986700_d4d4bf8c4b.jpg │ │ ├── 2741763055_9a7bb00802.jpg │ │ ├── 2745389517_250a397f31.jpg │ │ ├── 2751836205_6f7b5eff30.jpg │ │ ├── 2782079948_8d4e94a826.jpg │ │ ├── 2809496124_5f25b5946a.jpg │ │ ├── 2815838190_0a9889d995.jpg │ │ ├── 2841437312_789699c740.jpg │ │ ├── 2883093452_7e3a1eb53f.jpg │ │ ├── 290082189_f66cb80bfc.jpg │ │ ├── 296565463_d07a7bed96.jpg │ │ ├── 3077452620_548c79fda0.jpg │ │ ├── 348291597_ee836fbb1a.jpg │ │ ├── 350436573_41f4ecb6c8.jpg │ │ ├── 353266603_d3eac7e9a0.jpg │ │ ├── 372228424_16da1f8884.jpg │ │ ├── 400262091_701c00031c.jpg │ │ ├── 416144384_961c326481.jpg │ │ ├── 44105569_16720a960c.jpg │ │ ├── 456097971_860949c4fc.jpg │ │ ├── 464594019_1b24a28bb1.jpg │ │ ├── 485743562_d8cc6b8f73.jpg │ │ ├── 540976476_844950623f.jpg │ │ ├── 54736755_c057723f64.jpg │ │ ├── 57459255_752774f1b2.jpg │ │ ├── 576452297_897023f002.jpg │ │ ├── 586474709_ae436da045.jpg │ │ ├── 590318879_68cf112861.jpg │ │ ├── 59798110_2b6a3c8031.jpg │ │ ├── 603709866_a97c7cfc72.jpg │ │ ├── 603711658_4c8cd2201e.jpg │ │ ├── 65038344_52a45d090d.jpg │ │ ├── 6a00d8341c630a53ef00e553d0beb18834-800wi.jpg │ │ ├── 72100438_73de9f17af.jpg │ │ ├── 759745145_e8bc776ec8.jpg │ │ ├── 936182217_c4caa5222d.jpg │ │ └── abeja.jpg └── mnist │ ├── processed │ ├── test.pt │ └── training.pt │ └── raw │ ├── t10k-images-idx3-ubyte │ ├── t10k-labels-idx1-ubyte │ ├── train-images-idx3-ubyte │ └── train-labels-idx1-ubyte ├── modelTrainingVisualization ├── logger.py └── main.py ├── multiLabel └── multiLabel.py └── viewFeatureMap ├── husky.png └── viewAlexNetFeatureMap.py /AlexNet/AlexNetDeploy.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | 3 | import torch 4 | import torch.nn as nn 5 | from torchvision import datasets, models, transforms 6 | import os 7 | 8 | # 是否使用gpu运算 9 | use_gpu = torch.cuda.is_available() 10 | # 导入数据 11 | # 数据预处理 12 | data_transforms = { 13 | 'train': transforms.Compose([ 14 | transforms.RandomResizedCrop(224), 15 | transforms.RandomHorizontalFlip(), 16 | transforms.ToTensor(), 17 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 18 | ]), 19 | 'val': transforms.Compose([ 20 | transforms.Resize(256), 21 | transforms.CenterCrop(224), 22 | transforms.ToTensor(), 23 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 24 | ]), 25 | } 26 | 27 | # 读取数据要用的函数 28 | # 这个以后可以自己写 29 | data_dir = '../data/hymenoptera_data' 30 | image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), 31 | data_transforms[x]) 32 | for x in ['train', 'val']} 33 | dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, 34 | shuffle=True, num_workers=4) 35 | for x in ['train', 'val']} 36 | # 读取数据集大小 37 | dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} 38 | 39 | 40 | 41 | if __name__ == '__main__': 42 | # 导入Pytorch封装的AlexNet网络模型 43 | model = models.alexnet(pretrained=True) 44 | # 获取最后一个全连接层的输入通道数 45 | num_input = model.classifier[6].in_features 46 | # 获取全连接层的网络结构 47 | feature_model = list(model.classifier.children()) 48 | # 去掉原来的最后一层 49 | feature_model.pop() 50 | # 添加上适用于自己数据集的全连接层 51 | feature_model.append(nn.Linear(num_input, 2)) 52 | # 仿照这里的方法，可以修改网络的结构，不仅可以修改最后一个全连接层 53 | # 还可以为网络添加新的层 54 | # 重新生成网络的后半部分 55 | model.classifier = nn.Sequential(*feature_model) 56 | if use_gpu: 57 | model = model.cuda() 58 | model.load_state_dict(torch.load('model_AlexNet.pkl')) 59 | model.eval() 60 | # 取消测试阶段的梯度，避免out of memory 61 | with torch.no_grad(): 62 | for i, data in enumerate(dataloaders['val']): 63 | inputs, labels = data 64 | # 判断是否使用gpu 65 | if use_gpu: 66 | inputs = inputs.cuda() 67 | labels = labels.cuda() 68 | # 网络的前一部分 69 | information = model.features(inputs) 70 | # 拉成一维向量 71 | information = information.view(information.size(0), -1) 72 | # 提取倒数第二层的特征信息[0:5] 73 | # 提取倒数第三层的特征信息[0:4] 74 | # 提取网络的前半部分的特征信息model.features[0:n](information) 75 | information = model.classifier[0:5](information) 76 | print (information.shape) 77 | -------------------------------------------------------------------------------- /AlexNet/finetune_with_AlexNet.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | 3 | import torch 4 | import torch.nn as nn 5 | from torch.optim import lr_scheduler 6 | from torchvision import datasets, models, transforms 7 | import time 8 | import os 9 | import copy 10 | 11 | # 是否使用gpu运算 12 | use_gpu = torch.cuda.is_available() 13 | # 数据预处理 14 | data_transforms = { 15 | 'train': transforms.Compose([ 16 | # 随机在图像上裁剪出224*224大小的图像 17 | transforms.RandomResizedCrop(224), 18 | # 将图像随机翻转 19 | transforms.RandomHorizontalFlip(), 20 | # 将图像数据,转换为网络训练所需的tensor向量 21 | transforms.ToTensor(), 22 | # 图像归一化处理 23 | # 个人理解,前面是3个通道的均值,后面是3个通道的方差 24 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 25 | ]), 26 | 'val': transforms.Compose([ 27 | transforms.Resize(256), 28 | transforms.CenterCrop(224), 29 | transforms.ToTensor(), 30 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 31 | ]), 32 | } 33 | 34 | # 读取数据 35 | # 这种数据读取方法,需要有train和val两个文件夹， 36 | # 每个文件夹下一类图像存在一个文件夹下 37 | data_dir = '../data/hymenoptera_data' 38 | image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), 39 | data_transforms[x]) 40 | for x in ['train', 'val']} 41 | dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, 42 | shuffle=True, num_workers=4) 43 | for x in ['train', 'val']} 44 | 45 | # 读取数据集大小 46 | dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} 47 | # 数据类别 48 | class_names = image_datasets['train'].classes 49 | 50 | # 训练与验证网络（所有层都参加训练） 51 | def train_model(model, criterion, optimizer, scheduler, num_epochs=25): 52 | since = time.time() 53 | # 保存网络训练最好的权重 54 | best_model_wts = copy.deepcopy(model.state_dict()) 55 | best_acc = 0.0 56 | 57 | for epoch in range(num_epochs): 58 | print('Epoch {}/{}'.format(epoch, num_epochs - 1)) 59 | print('-' * 10) 60 | 61 | # 每训练一个epoch，测试一下网络模型的准确率 62 | for phase in ['train', 'val']: 63 | if phase == 'train': 64 | # 学习率更新方式 65 | scheduler.step() 66 | # 调用模型训练 67 | model.train(True) 68 | else: 69 | # 调用模型测试 70 | model.train(False) 71 | 72 | running_loss = 0.0 73 | running_corrects = 0 74 | # 依次获取所有图像，参与模型训练或测试 75 | for data in dataloaders[phase]: 76 | # 获取输入 77 | inputs, labels = data 78 | # 判断是否使用gpu 79 | if use_gpu: 80 | inputs = inputs.cuda() 81 | labels = labels.cuda() 82 | 83 | # 梯度清零 84 | optimizer.zero_grad() 85 | 86 | # 网络前向运行 87 | outputs = model(inputs) 88 | _, preds = torch.max(outputs.data, 1) 89 | # 计算Loss值 90 | loss = criterion(outputs, labels) 91 | 92 | # 反传梯度，更新权重 93 | if phase == 'train': 94 | # 反传梯度 95 | loss.backward() 96 | # 更新权重 97 | optimizer.step() 98 | 99 | # 计算一个epoch的loss值和准确率 100 | running_loss += loss.item() * inputs.size(0) 101 | running_corrects += torch.sum(preds == labels.data) 102 | # 计算Loss和准确率的均值 103 | epoch_loss = running_loss / dataset_sizes[phase] 104 | epoch_acc = float(running_corrects) / dataset_sizes[phase] 105 | 106 | print('{} Loss: {:.4f} Acc: {:.4f}'.format( 107 | phase, epoch_loss, epoch_acc)) 108 | 109 | # 保存测试阶段，准确率最高的模型 110 | if phase == 'val' and epoch_acc > best_acc: 111 | best_acc = epoch_acc 112 | best_model_wts = copy.deepcopy(model.state_dict()) 113 | 114 | time_elapsed = time.time() - since 115 | print('Training complete in {:.0f}m {:.0f}s'.format( 116 | time_elapsed // 60, time_elapsed % 60)) 117 | print('Best val Acc: {:4f}'.format(best_acc)) 118 | # 网络导入最好的网络权重 119 | model.load_state_dict(best_model_wts) 120 | return model 121 | 122 | # 精调AlexNet 123 | if __name__ == '__main__': 124 | 125 | # 导入Pytorch封装的AlexNet网络模型 126 | model = models.alexnet(pretrained=True) 127 | # 获取最后一个全连接层的输入通道数 128 | num_input = model.classifier[6].in_features 129 | # 获取全连接层的网络结构 130 | feature_model = list(model.classifier.children()) 131 | # 去掉原来的最后一层 132 | feature_model.pop() 133 | # 添加上适用于自己数据集的全连接层 134 | feature_model.append(nn.Linear(num_input, 2)) 135 | # 仿照这里的方法，可以修改网络的结构，不仅可以修改最后一个全连接层 136 | # 还可以为网络添加新的层 137 | # 重新生成网络的后半部分 138 | model.classifier = nn.Sequential(*feature_model) 139 | if use_gpu: 140 | model = model.cuda() 141 | # 定义损失函数 142 | criterion = nn.CrossEntropyLoss() 143 | 144 | # 为不同层设定不同的学习率 145 | fc_params = list(map(id, model.classifier[6].parameters())) 146 | base_params = filter(lambda p: id(p) not in fc_params, model.parameters()) 147 | params = [{"params": base_params, "lr":0.0001}, 148 | {"params": model.classifier[6].parameters(), "lr":0.001},] 149 | optimizer_ft = torch.optim.SGD(params, momentum=0.9) 150 | 151 | # 定义学习率的更新方式，每5个epoch修改一次学习率 152 | exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=5, gamma=0.1) 153 | model = train_model(model, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=10) 154 | torch.save(model.state_dict(),"model_AlexNet.pkl") 155 | -------------------------------------------------------------------------------- /AlexNet/read_AlexNet_structure.py: -------------------------------------------------------------------------------- 1 | import torchvision.models as models 2 | 3 | # 导入Pytorch所封装的AlexNet模型 4 | model = models.alexnet(pretrained=True) 5 | 6 | # model.features存储的是网络模型的前半部分 7 | # 即卷积层与池化层部分 8 | model_features = list(model.features.children()) 9 | for feature in model_features: 10 | print (feature) 11 | # model.classifier存储的是模型的后半部分 12 | # 即全连接层部分 13 | model_classifier = list(model.classifier.children()) 14 | for classifier in model_classifier: 15 | print (classifier) 16 | 17 | 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-------------------------------------------------------------------------------- /LeNet5/lenet.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | from collections import OrderedDict 3 | 4 | 5 | class LeNet5(nn.Module): 6 | def __init__(self): 7 | super(LeNet5, self).__init__() 8 | # 定义网络的前半部分卷积、池化、激活函数 9 | # (1, 6, kernel_size=(5, 5)))， 10 | # 1：输入数据的通道数 11 | # 6：卷积核个数 12 | # (1, 6, kernel_size=(5, 5)))卷积核的尺寸 13 | # stride=2步长,默认为1 14 | # nn.Sequential,将若干个功能层的功能组合到一起 15 | self.convnet = nn.Sequential(OrderedDict([ 16 | ('c1', nn.Conv2d(1, 6, kernel_size=(5, 5))), 17 | ('relu1', nn.ReLU()), 18 | ('s2', nn.MaxPool2d(kernel_size=(2, 2), stride=2)), 19 | ('c3', nn.Conv2d(6, 16, kernel_size=(5, 5))), 20 | ('relu3', nn.ReLU()), 21 | ('s4', nn.MaxPool2d(kernel_size=(2, 2), stride=2)), 22 | ('c5', nn.Conv2d(16, 120, kernel_size=(5, 5))), 23 | ('relu5', nn.ReLU()) 24 | ])) 25 | 26 | # 定义后半部分的全连接层 27 | self.fc = nn.Sequential(OrderedDict([ 28 | ('f6', nn.Linear(120, 84)), 29 | ('relu6', nn.ReLU()), 30 | ('f7', nn.Linear(84, 10)), 31 | ('sig7', nn.LogSoftmax(dim=-1)) 32 | ])) 33 | # 定义网络的前向运算 34 | def forward(self, img): 35 | output = self.convnet(img) 36 | # 在第一个全连接层与卷积层连接的位置 37 | # 需要将特征图拉成一个一维向量 38 | output = output.view(img.size(0), -1) 39 | output = self.fc(output) 40 | return output 41 | -------------------------------------------------------------------------------- /LeNet5/mnist_deploy.py: -------------------------------------------------------------------------------- 1 | from lenet import LeNet5 2 | import torch 3 | from torchvision.datasets.mnist import MNIST 4 | import torchvision.transforms as transforms 5 | from torch.utils.data import DataLoader 6 | 7 | # 读取数据 8 | data_test = MNIST('../data/mnist', 9 | train=False, 10 | download=True, 11 | transform=transforms.Compose([ 12 | transforms.Resize((32, 32)), 13 | transforms.ToTensor()])) 14 | 15 | # num_workers=8 使用多进程加载数据 16 | data_test_loader = DataLoader(data_test, batch_size=1024, num_workers=8) 17 | 18 | # 初始化网络 19 | net = LeNet5() 20 | net.load_state_dict(torch.load(r'model/1_model.pkl')) 21 | 22 | def deploy(): 23 | # 验证阶段 24 | net.eval() 25 | total_correct = 0 26 | # 取消测试阶段的梯度，避免out of memory 27 | with torch.no_grad(): 28 | for i, (images, labels) in enumerate(data_test_loader): 29 | output = net(images) 30 | # 计算准确率 31 | pred = output.detach().max(1)[1] 32 | total_correct += pred.eq(labels.view_as(pred)).sum() 33 | print('Accuracy: %f' % ( float(total_correct) / len(data_test))) 34 | 35 | 36 | def main(): 37 | deploy() 38 | 39 | 40 | if __name__ == '__main__': 41 | main() 42 | -------------------------------------------------------------------------------- /LeNet5/run.py: -------------------------------------------------------------------------------- 1 | from lenet import LeNet5 2 | import torch 3 | import torch.nn as nn 4 | import torch.optim as optim 5 | from torchvision.datasets.mnist import MNIST 6 | import torchvision.transforms as transforms 7 | from torch.utils.data import DataLoader 8 | 9 | # 读取数据 10 | data_train = MNIST('../data/mnist', 11 | download=True, 12 | transform=transforms.Compose([ 13 | transforms.Resize((32, 32)), 14 | transforms.ToTensor()])) 15 | data_test = MNIST('../data/mnist', 16 | train=False, 17 | download=True, 18 | transform=transforms.Compose([ 19 | transforms.Resize((32, 32)), 20 | transforms.ToTensor()])) 21 | 22 | # num_workers=8 使用多进程加载数据 23 | data_train_loader = DataLoader(data_train, batch_size=256, shuffle=True, num_workers=8) 24 | data_test_loader = DataLoader(data_test, batch_size=1024, num_workers=8) 25 | 26 | # 初始化网络 27 | net = LeNet5() 28 | 29 | # 定义损失函数 30 | criterion = nn.CrossEntropyLoss() 31 | # 定义网络优化方法 32 | optimizer = optim.Adam(net.parameters(), lr=2e-3) 33 | 34 | # 定义训练阶段 35 | def train(epoch): 36 | net.train() 37 | loss_list, batch_list = [], [] 38 | for i, (images, labels) in enumerate(data_train_loader): 39 | # 初始0梯度 40 | optimizer.zero_grad() 41 | # 网络前向运行 42 | output = net(images) 43 | # 计算网络的损失函数 44 | loss = criterion(output, labels) 45 | 46 | # 存储每一次的梯度与迭代次数 47 | loss_list.append(loss.detach().cpu().item()) 48 | batch_list.append(i+1) 49 | 50 | if i % 10 == 0: 51 | print('Train - Epoch %d, Batch: %d, Loss: %f' % (epoch, i, loss.detach().cpu().item())) 52 | # 反向传播梯度 53 | loss.backward() 54 | # 优化更新权重 55 | optimizer.step() 56 | # 保存网络模型结构 57 | torch.save(net.state_dict(), 'model//' + str(epoch) + '_model.pkl') 58 | 59 | 60 | 61 | def test(): 62 | # 验证阶段 63 | net.eval() 64 | total_correct = 0 65 | avg_loss = 0.0 66 | # 取消梯度，避免测试阶段out of memory 67 | with torch.no_grad(): 68 | for i, (images, labels) in enumerate(data_test_loader): 69 | output = net(images) 70 | avg_loss += criterion(output, labels).sum() 71 | # 计算准确率 72 | pred = output.detach().max(1)[1] 73 | total_correct += pred.eq(labels.view_as(pred)).sum() 74 | 75 | avg_loss /= len(data_test) 76 | print('Test Avg. Loss: %f, Accuracy: %f' % (avg_loss.detach().cpu().item(), float(total_correct) / len(data_test))) 77 | 78 | 79 | def train_and_test(epoch): 80 | train(epoch) 81 | test() 82 | 83 | 84 | def main(): 85 | for e in range(1, 16): 86 | train_and_test(e) 87 | 88 | 89 | if __name__ == '__main__': 90 | main() -------------------------------------------------------------------------------- /Self_DataLoader/load_data_by_folder.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | 3 | import torch 4 | from torchvision import datasets, models, transforms 5 | import os 6 | 7 | # 定义数据预处理步骤 8 | data_transforms = { 9 | 'train': transforms.Compose([ 10 | # 随机在图像上裁剪出224*224大小的图像 11 | transforms.RandomResizedCrop(224), 12 | # 将图像随机翻转 13 | transforms.RandomHorizontalFlip(), 14 | # 将图像数据,转换为网络训练所需的tensor向量 15 | transforms.ToTensor(), 16 | # 图像归一化处理 17 | # 个人理解,前面是3个通道的均值,后面是3个通道的方差 18 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 19 | ]), 20 | 'val': transforms.Compose([ 21 | transforms.Resize(256), 22 | transforms.CenterCrop(224), 23 | transforms.ToTensor(), 24 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 25 | ]), 26 | } 27 | 28 | # 读取数据 29 | # 数据路径 30 | data_dir = '../data/hymenoptera_data' 31 | # 调用torchvision.datasets.ImageFolder,实现图像数据的读入和预处理 32 | image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), 33 | data_transforms[x]) 34 | for x in ['train', 'val']} 35 | # 调用torch.utils.data.DataLoader，生成Pytorch输入所需的DataLoader格式 36 | dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, 37 | shuffle=True, num_workers=4) 38 | for x in ['train', 'val']} 39 | # 读取数据集大小 40 | dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} 41 | # 数据类别 42 | class_names = image_datasets['train'].classes 43 | 44 | if __name__ == '__main__': 45 | for x in dataloaders['train']: 46 | inputs, label = x 47 | print(inputs) 48 | print(label) 49 | -------------------------------------------------------------------------------- /Self_DataLoader/load_data_by_txt.py: -------------------------------------------------------------------------------- 1 | from torchvision import transforms, utils 2 | from PIL import Image 3 | import torch.nn as nn 4 | import os 5 | from torch.utils.data import Dataset, DataLoader 6 | 7 | # 定义数据的处理方式 8 | data_transforms = { 9 | 'train': transforms.Compose([ 10 | # 将图像进行缩放，缩放为256*256 11 | transforms.Resize(256), 12 | # 在256*256的图像上随机裁剪出224*224大小的图像用于训练 13 | transforms.RandomResizedCrop(224), 14 | # 图像用于翻转 15 | transforms.RandomHorizontalFlip(), 16 | # 转换成tensor向量 17 | transforms.ToTensor(), 18 | # 对图像进行归一化操作 19 | # [0.485, 0.456, 0.406]，RGB通道的均值与标准差 20 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 21 | ]), 22 | # 测试集需要中心裁剪，甚至不裁剪，直接缩放为224*224for，不需要翻转 23 | 'val': transforms.Compose([ 24 | transforms.Resize(256), 25 | transforms.CenterCrop(224), 26 | transforms.ToTensor(), 27 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 28 | ]), 29 | } 30 | 31 | 32 | # 定义数据读入 33 | def Load_Image_Information(path): 34 | # 图像存储路径 35 | image_Root_Dir = r'数据集图像路径' 36 | # 获取图像的路径 37 | iamge_Dir = os.path.join(image_Root_Dir, path) 38 | # 以RGB格式打开图像 39 | # Pytorch DataLoader就是使用PIL所读取的图像格式 40 | # 建议就用这种方法读取图像，当读入灰度图像时convert('') 41 | return Image.open(iamge_Dir).convert('RGB') 42 | 43 | 44 | # 定义自己数据集的数据读入类 45 | class my_Data_Set(nn.Module): 46 | def __init__(self, txt, transform=None, target_transform=None, loader=None): 47 | super(my_Data_Set, self).__init__() 48 | # 打开存储图像名与标签的txt文件 49 | fp = open(txt, 'r') 50 | images = [] 51 | labels = [] 52 | # 将图像名和图像标签对应存储起来 53 | for line in fp: 54 | line.strip('\n') 55 | line.rstrip() 56 | information = line.split() 57 | images.append(information[0]) 58 | labels.append(int(information[1])) 59 | self.images = images 60 | self.labels = labels 61 | self.transform = transform 62 | self.target_transform = target_transform 63 | self.loader = loader 64 | 65 | # 重写这个函数用来进行图像数据的读取 66 | def __getitem__(self, item): 67 | # 获取图像名和标签 68 | imageName = self.images[item] 69 | label = self.labels[item] 70 | # 读入图像信息 71 | image = self.loader(imageName) 72 | # 处理图像数据 73 | if self.transform is not None: 74 | image = self.transform(image) 75 | return image, label 76 | 77 | # 重写这个函数，来看数据集中含有多少数据 78 | def __len__(self): 79 | return len(self.images) 80 | 81 | 82 | # 生成Pytorch所需的DataLoader数据输入格式 83 | train_Data = my_Data_Set('train.txt路径', transform=data_transforms['train'], loader=Load_Image_Information) 84 | val_Data = my_Data_Set('val.txt路径', transform=data_transforms['val'], loader=Load_Image_Information) 85 | train_DataLoader = DataLoader(train_Data, batch_size=10, shuffle=True) 86 | val_DataLoader = DataLoader(val_Data, batch_size=10) 87 | 88 | # 验证是否生成DataLoader格式数据 89 | for data in train_DataLoader: 90 | inputs, labels = data 91 | print(inputs) 92 | print(labels) 93 | for data in val_DataLoader: 94 | inputs, labels = data 95 | print(inputs) 96 | print(labels) -------------------------------------------------------------------------------- /TransferLearning/Finetune_with_different_lr.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | 3 | import torch 4 | import torch.nn as nn 5 | import torch.optim as optim 6 | from torch.optim import lr_scheduler 7 | import numpy as np 8 | import torchvision 9 | from torchvision import datasets, models, transforms 10 | import time 11 | import os 12 | import copy 13 | 14 | # 是否使用gpu运算 15 | use_gpu = torch.cuda.is_available() 16 | # 数据预处理 17 | data_transforms = { 18 | 'train': transforms.Compose([ 19 | # 随机在图像上裁剪出224*224大小的图像 20 | transforms.RandomResizedCrop(224), 21 | # 将图像随机翻转 22 | transforms.RandomHorizontalFlip(), 23 | # 将图像数据,转换为网络训练所需的tensor向量 24 | transforms.ToTensor(), 25 | # 图像归一化处理 26 | # 个人理解,前面是3个通道的均值,后面是3个通道的方差 27 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 28 | ]), 29 | 'val': transforms.Compose([ 30 | transforms.Resize(256), 31 | transforms.CenterCrop(224), 32 | transforms.ToTensor(), 33 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 34 | ]), 35 | } 36 | 37 | # 读取数据 38 | # 这种数据读取方法,需要有train和val两个文件夹， 39 | # 每个文件夹下一类图像存在一个文件夹下 40 | data_dir = '../data/hymenoptera_data' 41 | image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), 42 | data_transforms[x]) 43 | for x in ['train', 'val']} 44 | dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, 45 | shuffle=True, num_workers=4) 46 | for x in ['train', 'val']} 47 | 48 | # 读取数据集大小 49 | dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} 50 | # 数据类别 51 | class_names = image_datasets['train'].classes 52 | 53 | # 训练与验证网络（所有层都参加训练） 54 | def train_model(model, criterion, optimizer, scheduler, num_epochs=25): 55 | since = time.time() 56 | # 保存网络训练最好的权重 57 | best_model_wts = copy.deepcopy(model.state_dict()) 58 | best_acc = 0.0 59 | 60 | for epoch in range(num_epochs): 61 | print('Epoch {}/{}'.format(epoch, num_epochs - 1)) 62 | print('-' * 10) 63 | 64 | # 每训练一个epoch，测试一下网络模型的准确率 65 | for phase in ['train', 'val']: 66 | if phase == 'train': 67 | # 学习率更新方式 68 | scheduler.step() 69 | # 调用模型训练 70 | model.train(True) 71 | else: 72 | # 调用模型测试 73 | model.train(False) 74 | 75 | running_loss = 0.0 76 | running_corrects = 0 77 | # 依次获取所有图像，参与模型训练或测试 78 | for data in dataloaders[phase]: 79 | # 获取输入 80 | inputs, labels = data 81 | # 判断是否使用gpu 82 | if use_gpu: 83 | inputs = inputs.cuda() 84 | labels = labels.cuda() 85 | 86 | # 梯度清零 87 | optimizer.zero_grad() 88 | 89 | # 网络前向运行 90 | outputs = model(inputs) 91 | _, preds = torch.max(outputs.data, 1) 92 | # 计算Loss值 93 | loss = criterion(outputs, labels) 94 | 95 | # 反传梯度，更新权重 96 | if phase == 'train': 97 | # 反传梯度 98 | loss.backward() 99 | # 更新权重 100 | optimizer.step() 101 | 102 | # 计算一个epoch的loss值和准确率 103 | running_loss += loss.item() * inputs.size(0) 104 | running_corrects += torch.sum(preds == labels.data) 105 | # 计算Loss和准确率的均值 106 | epoch_loss = running_loss / dataset_sizes[phase] 107 | epoch_acc = float(running_corrects) / dataset_sizes[phase] 108 | 109 | print('{} Loss: {:.4f} Acc: {:.4f}'.format( 110 | phase, epoch_loss, epoch_acc)) 111 | 112 | # 保存测试阶段，准确率最高的模型 113 | if phase == 'val' and epoch_acc > best_acc: 114 | best_acc = epoch_acc 115 | best_model_wts = copy.deepcopy(model.state_dict()) 116 | 117 | time_elapsed = time.time() - since 118 | print('Training complete in {:.0f}m {:.0f}s'.format( 119 | time_elapsed // 60, time_elapsed % 60)) 120 | print('Best val Acc: {:4f}'.format(best_acc)) 121 | # 网络导入最好的网络权重 122 | model.load_state_dict(best_model_wts) 123 | return model 124 | 125 | # 微调网络 126 | if __name__ == '__main__': 127 | 128 | # 导入Pytorch中自带的resnet18网络模型 129 | model_ft = models.resnet18(pretrained=True) 130 | 131 | # 修改网络模型的最后一个全连接层 132 | # 获取最后一个全连接层的输入通道数 133 | num_ftrs = model_ft.fc.in_features 134 | # 修改最后一个全连接层的的输出数为2 135 | model_ft.fc = nn.Linear(num_ftrs, 2) 136 | 137 | # 是否使用gpu 138 | if use_gpu: 139 | model_ft = model_ft.cuda() 140 | 141 | # 定义网络模型的损失函数 142 | criterion = nn.CrossEntropyLoss() 143 | 144 | # 为不同层设定不同的学习率 145 | fc_params = list(map(id, model_ft.fc.parameters())) 146 | # 除全连接层的，其它层参数 147 | base_params = filter(lambda p: id(p) not in fc_params, model_ft.parameters()) 148 | params = [{"params": base_params, "lr":0.0001}, 149 | {"params": model_ft.fc.parameters(), "lr":0.001},] 150 | optimizer_ft = torch.optim.SGD(params, momentum=0.9) 151 | 152 | # 定义学习率的更新方式，每5个epoch修改一次学习率 153 | exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=5, gamma=0.1) 154 | # 训练网络模型 155 | model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=10) 156 | # 存储网络模型的权重 157 | torch.save(model_ft.state_dict(),"model_different_lr.pkl") 158 | 159 | 160 | -------------------------------------------------------------------------------- /TransferLearning/finetune_Fc_Layer.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | 3 | import torch 4 | import torch.nn as nn 5 | from torch.optim import lr_scheduler 6 | from torchvision import datasets, models, transforms 7 | import time 8 | import os 9 | import copy 10 | 11 | # 是否使用gpu运算 12 | use_gpu = torch.cuda.is_available() 13 | # 数据预处理 14 | data_transforms = { 15 | 'train': transforms.Compose([ 16 | # 随机在图像上裁剪出224*224大小的图像 17 | transforms.RandomResizedCrop(224), 18 | # 将图像随机翻转 19 | transforms.RandomHorizontalFlip(), 20 | # 将图像数据,转换为网络训练所需的tensor向量 21 | transforms.ToTensor(), 22 | # 图像归一化处理 23 | # 个人理解,前面是3个通道的均值,后面是3个通道的方差 24 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 25 | ]), 26 | 'val': transforms.Compose([ 27 | transforms.Resize(256), 28 | transforms.CenterCrop(224), 29 | transforms.ToTensor(), 30 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 31 | ]), 32 | } 33 | 34 | # 读取数据 35 | # 这种数据读取方法,需要有train和val两个文件夹， 36 | # 每个文件夹下一类图像存在一个文件夹下 37 | data_dir = '../data/hymenoptera_data' 38 | image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), 39 | data_transforms[x]) 40 | for x in ['train', 'val']} 41 | dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, 42 | shuffle=True, num_workers=4) 43 | for x in ['train', 'val']} 44 | 45 | # 读取数据集大小 46 | dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} 47 | # 数据类别 48 | class_names = image_datasets['train'].classes 49 | 50 | # 训练与验证网络（所有层都参加训练） 51 | def train_model(model, criterion, optimizer, scheduler, num_epochs=25): 52 | since = time.time() 53 | # 保存网络训练最好的权重 54 | best_model_wts = copy.deepcopy(model.state_dict()) 55 | best_acc = 0.0 56 | 57 | for epoch in range(num_epochs): 58 | print('Epoch {}/{}'.format(epoch, num_epochs - 1)) 59 | print('-' * 10) 60 | 61 | # 每训练一个epoch，测试一下网络模型的准确率 62 | for phase in ['train', 'val']: 63 | if phase == 'train': 64 | # 学习率更新方式 65 | scheduler.step() 66 | # 调用模型训练 67 | model.train(True) 68 | else: 69 | # 调用模型测试 70 | model.train(False) 71 | 72 | running_loss = 0.0 73 | running_corrects = 0 74 | # 依次获取所有图像，参与模型训练或测试 75 | for data in dataloaders[phase]: 76 | # 获取输入 77 | inputs, labels = data 78 | # 判断是否使用gpu 79 | if use_gpu: 80 | inputs = inputs.cuda() 81 | labels = labels.cuda() 82 | 83 | # 梯度清零 84 | optimizer.zero_grad() 85 | 86 | # 网络前向运行 87 | outputs = model(inputs) 88 | _, preds = torch.max(outputs.data, 1) 89 | # 计算Loss值 90 | loss = criterion(outputs, labels) 91 | 92 | # 反传梯度，更新权重 93 | if phase == 'train': 94 | # 反传梯度 95 | loss.backward() 96 | # 更新权重 97 | optimizer.step() 98 | 99 | # 计算一个epoch的loss值和准确率 100 | running_loss += loss.item() * inputs.size(0) 101 | running_corrects += torch.sum(preds == labels.data) 102 | # 计算Loss和准确率的均值 103 | epoch_loss = running_loss / dataset_sizes[phase] 104 | epoch_acc = float(running_corrects) / dataset_sizes[phase] 105 | 106 | print('{} Loss: {:.4f} Acc: {:.4f}'.format( 107 | phase, epoch_loss, epoch_acc)) 108 | 109 | # 保存测试阶段，准确率最高的模型 110 | if phase == 'val' and epoch_acc > best_acc: 111 | best_acc = epoch_acc 112 | best_model_wts = copy.deepcopy(model.state_dict()) 113 | 114 | time_elapsed = time.time() - since 115 | print('Training complete in {:.0f}m {:.0f}s'.format( 116 | time_elapsed // 60, time_elapsed % 60)) 117 | print('Best val Acc: {:4f}'.format(best_acc)) 118 | # 网络导入最好的网络权重 119 | model.load_state_dict(best_model_wts) 120 | return model 121 | 122 | # 微调网络 123 | if __name__ == '__main__': 124 | 125 | # 导入Pytorch中自带的resnet18网络模型 126 | model_ft = models.resnet18(pretrained=True) 127 | # 将网络模型的各层的梯度更新置为False 128 | for param in model_ft.parameters(): 129 | param.requires_grad = False 130 | 131 | # 修改网络模型的最后一个全连接层 132 | # 获取最后一个全连接层的输入通道数 133 | num_ftrs = model_ft.fc.in_features 134 | # 修改最后一个全连接层的的输出数为2 135 | model_ft.fc = nn.Linear(num_ftrs, 2) 136 | # 是否使用gpu 137 | if use_gpu: 138 | model_ft = model_ft.cuda() 139 | 140 | # 定义网络模型的损失函数 141 | criterion = nn.CrossEntropyLoss() 142 | 143 | # 只训练最后一个层 144 | # 采用随机梯度下降的方式，来优化网络模型 145 | optimizer_ft = torch.optim.SGD(model_ft.fc.parameters(), lr=0.001, momentum=0.9) 146 | 147 | # 定义学习率的更新方式，每5个epoch修改一次学习率 148 | exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=5, gamma=0.1) 149 | # 训练网络模型 150 | model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=10) 151 | # 存储网络模型的权重 152 | torch.save(model_ft.state_dict(),"model_only_fc.pkl") 153 | 154 | -------------------------------------------------------------------------------- /cancel_val_grad/cancel_val_grad_method_function.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | 3 | import torch 4 | import torch.nn as nn 5 | from torch.optim import lr_scheduler 6 | from torchvision import datasets, models, transforms 7 | import time 8 | import os 9 | import copy 10 | 11 | # 是否使用gpu运算 12 | use_gpu = torch.cuda.is_available() 13 | # 数据预处理 14 | data_transforms = { 15 | 'train': transforms.Compose([ 16 | # 随机在图像上裁剪出224*224大小的图像 17 | transforms.RandomResizedCrop(224), 18 | # 将图像随机翻转 19 | transforms.RandomHorizontalFlip(), 20 | # 将图像数据,转换为网络训练所需的tensor向量 21 | transforms.ToTensor(), 22 | # 图像归一化处理 23 | # 个人理解,前面是3个通道的均值,后面是3个通道的方差 24 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 25 | ]), 26 | 'val': transforms.Compose([ 27 | transforms.Resize(256), 28 | transforms.CenterCrop(224), 29 | transforms.ToTensor(), 30 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 31 | ]), 32 | } 33 | 34 | # 读取数据 35 | # 这种数据读取方法,需要有train和val两个文件夹， 36 | # 每个文件夹下一类图像存在一个文件夹下 37 | data_dir = '../data/hymenoptera_data' 38 | image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), 39 | data_transforms[x]) 40 | for x in ['train', 'val']} 41 | dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, 42 | shuffle=True, num_workers=4) 43 | for x in ['train', 'val']} 44 | 45 | # 读取数据集大小 46 | dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} 47 | # 数据类别 48 | class_names = image_datasets['train'].classes 49 | 50 | # 定义训练阶段 51 | def train(model, criterion, optimizer, scheduler, phase='train'): 52 | # 保存训练一个epoch的Loss值与准确率 53 | running_loss = 0.0 54 | running_corrects = 0 55 | # 更新学习率 56 | scheduler.step() 57 | # 指定模型训练 58 | model.train() 59 | for data in dataloaders[phase]: 60 | inputs, labels = data 61 | 62 | # 判断是否使用gpu 63 | if use_gpu: 64 | inputs = inputs.cuda() 65 | labels = labels.cuda() 66 | 67 | # 清除梯度 68 | optimizer.zero_grad() 69 | # 模型前向运行 70 | outputs = model(inputs) 71 | # 计算预测结果 72 | _, preds = torch.max(outputs.data, 1) 73 | # 计算Loss值 74 | loss = criterion(outputs, labels) 75 | # 反传loss 76 | loss.backward() 77 | # 更新模型权重 78 | optimizer.step() 79 | # 统计Loss值 80 | running_loss += loss.item() * inputs.size(0) 81 | running_corrects += torch.sum(preds == labels.data) 82 | epoch_loss = running_loss / dataset_sizes[phase] 83 | epoch_acc = float(running_corrects) / dataset_sizes[phase] 84 | print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc)) 85 | return model 86 | 87 | 88 | # 定义验证阶段 89 | def val(model, criterion, phase='val'): 90 | # 模型验证 91 | model.eval() 92 | # 指定不保存梯度 93 | with torch.no_grad(): 94 | # 统计Loss值与准确率 95 | running_loss = 0.0 96 | running_corrects = 0 97 | 98 | for data in dataloaders[phase]: 99 | inputs, labels = data 100 | # 判断是否使用gpu 101 | if use_gpu: 102 | inputs = inputs.cuda() 103 | labels = labels.cuda() 104 | 105 | # 模型前向运行 106 | outputs = model(inputs) 107 | # 获取预测结果 108 | _, preds = torch.max(outputs.data, 1) 109 | # 计算Loss值 110 | loss = criterion(outputs, labels) 111 | # 统计Loss值和准确率 112 | running_loss += loss.item() * inputs.size(0) 113 | running_corrects += torch.sum(preds == labels.data) 114 | epoch_loss = running_loss / dataset_sizes[phase] 115 | epoch_acc = float(running_corrects) / dataset_sizes[phase] 116 | print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc)) 117 | return epoch_acc 118 | 119 | # 定义网络训练（中间夹杂着验证） 120 | def train_model(model, criterion, optimizer, scheduler, num_epochs=25): 121 | since = time.time() 122 | # 保存网络训练最好的权重 123 | best_model_wts = copy.deepcopy(model.state_dict()) 124 | best_acc = 0.0 125 | 126 | for epoch in range(num_epochs): 127 | print('Epoch {}/{}'.format(epoch, num_epochs - 1)) 128 | print('-' * 10) 129 | 130 | model = train(model, criterion, optimizer, scheduler) 131 | acc = val(model, criterion) 132 | 133 | # 保存测试阶段，准确率最高的模型 134 | if acc > best_acc: 135 | best_acc = acc 136 | best_model_wts = copy.deepcopy(model.state_dict()) 137 | time_elapsed = time.time() - since 138 | print('Training complete in {:.0f}m {:.0f}s'.format( 139 | time_elapsed // 60, time_elapsed % 60)) 140 | print('Best val Acc: {:4f}'.format(best_acc)) 141 | # 网络导入最好的网络权重 142 | model.load_state_dict(best_model_wts) 143 | return model 144 | 145 | 146 | # 微调网络 147 | if __name__ == '__main__': 148 | 149 | # 导入Pytorch中自带的resnet18网络模型 150 | model_ft = models.resnet18(pretrained=True) 151 | # 将网络模型的各层的梯度更新置为False 152 | for param in model_ft.parameters(): 153 | param.requires_grad = False 154 | 155 | # 修改网络模型的最后一个全连接层 156 | # 获取最后一个全连接层的输入通道数 157 | num_ftrs = model_ft.fc.in_features 158 | # 修改最后一个全连接层的的输出数为2 159 | model_ft.fc = nn.Linear(num_ftrs, 2) 160 | # 是否使用gpu 161 | if use_gpu: 162 | model_ft = model_ft.cuda() 163 | 164 | # 定义网络模型的损失函数 165 | criterion = nn.CrossEntropyLoss() 166 | 167 | # 只训练最后一个层 168 | # 采用随机梯度下降的方式，来优化网络模型 169 | optimizer_ft = torch.optim.SGD(model_ft.fc.parameters(), lr=0.001, momentum=0.9) 170 | 171 | # 定义学习率的更新方式，每5个epoch修改一次学习率 172 | exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=2, gamma=0.1) 173 | # 训练网络模型 174 | model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=10) 175 | # 存储网络模型的权重 176 | torch.save(model_ft.state_dict(),"model_only_fc.pkl") 177 | -------------------------------------------------------------------------------- /cancel_val_grad/cancel_val_grad_method_together.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | 3 | import torch 4 | import torch.nn as nn 5 | from torch.optim import lr_scheduler 6 | from torchvision import datasets, models, transforms 7 | import time 8 | import os 9 | import copy 10 | 11 | # 是否使用gpu运算 12 | use_gpu = torch.cuda.is_available() 13 | # 数据预处理 14 | data_transforms = { 15 | 'train': transforms.Compose([ 16 | # 随机在图像上裁剪出224*224大小的图像 17 | transforms.RandomResizedCrop(224), 18 | # 将图像随机翻转 19 | transforms.RandomHorizontalFlip(), 20 | # 将图像数据,转换为网络训练所需的tensor向量 21 | transforms.ToTensor(), 22 | # 图像归一化处理 23 | # 个人理解,前面是3个通道的均值,后面是3个通道的方差 24 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 25 | ]), 26 | 'val': transforms.Compose([ 27 | transforms.Resize(256), 28 | transforms.CenterCrop(224), 29 | transforms.ToTensor(), 30 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 31 | ]), 32 | } 33 | 34 | # 读取数据 35 | # 这种数据读取方法,需要有train和val两个文件夹， 36 | # 每个文件夹下一类图像存在一个文件夹下 37 | data_dir = '../data/hymenoptera_data' 38 | image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), 39 | data_transforms[x]) 40 | for x in ['train', 'val']} 41 | dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, 42 | shuffle=True, num_workers=4) 43 | for x in ['train', 'val']} 44 | 45 | # 读取数据集大小 46 | dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} 47 | # 数据类别 48 | class_names = image_datasets['train'].classes 49 | 50 | # 训练与验证网络（所有层都参加训练） 51 | def train_model(model, criterion, optimizer, scheduler, num_epochs=25): 52 | since = time.time() 53 | # 保存网络训练最好的权重 54 | best_model_wts = copy.deepcopy(model.state_dict()) 55 | best_acc = 0.0 56 | 57 | for epoch in range(num_epochs): 58 | print('Epoch {}/{}'.format(epoch, num_epochs - 1)) 59 | print('-' * 10) 60 | 61 | # 每训练一个epoch，测试一下网络模型的准确率 62 | for phase in ['train', 'val']: 63 | 64 | running_loss = 0.0 65 | running_corrects = 0 66 | 67 | if phase == 'train': 68 | # 学习率更新方式 69 | scheduler.step() 70 | print (scheduler.get_lr()) 71 | # 调用模型训练 72 | model.train(True) 73 | # 依次获取所有图像，参与模型训练或测试 74 | for data in dataloaders[phase]: 75 | # 获取输入 76 | inputs, labels = data 77 | 判断是否使用gpu 78 | if use_gpu: 79 | inputs = inputs.cuda() 80 | labels = labels.cuda() 81 | 82 | # 梯度清零 83 | optimizer.zero_grad() 84 | 85 | # 网络前向运行 86 | outputs = model(inputs) 87 | _, preds = torch.max(outputs.data, 1) 88 | # 计算Loss值 89 | loss = criterion(outputs, labels) 90 | # 反传梯度 91 | loss.backward() 92 | # 更新权重 93 | optimizer.step() 94 | # 计算一个epoch的loss值和准确率 95 | running_loss += loss.item() * inputs.size(0) 96 | running_corrects += torch.sum(preds == labels.data) 97 | else: 98 | # 定义不保存梯度 99 | with torch.no_grad(): 100 | # 调用模型测试 101 | model.eval() 102 | # 依次获取所有图像，参与模型训练或测试 103 | for data in dataloaders[phase]: 104 | # 获取输入 105 | inputs, labels = data 106 | # 判断是否使用gpu 107 | if use_gpu: 108 | inputs = inputs.cuda() 109 | labels = labels.cuda() 110 | 111 | # 网络前向运行 112 | outputs = model(inputs) 113 | _, preds = torch.max(outputs.data, 1) 114 | # 计算Loss值 115 | loss = criterion(outputs, labels) 116 | # 计算一个epoch的loss值和准确率 117 | running_loss += loss.item() * inputs.size(0) 118 | running_corrects += torch.sum(preds == labels.data) 119 | # 计算Loss和准确率的均值 120 | epoch_loss = running_loss / dataset_sizes[phase] 121 | epoch_acc = float(running_corrects) / dataset_sizes[phase] 122 | 123 | print('{} Loss: {:.4f} Acc: {:.4f}'.format( 124 | phase, epoch_loss, epoch_acc)) 125 | 126 | # 保存测试阶段，准确率最高的模型 127 | if phase == 'val' and epoch_acc > best_acc: 128 | best_acc = epoch_acc 129 | best_model_wts = copy.deepcopy(model.state_dict()) 130 | time_elapsed = time.time() - since 131 | print('Training complete in {:.0f}m {:.0f}s'.format( 132 | time_elapsed // 60, time_elapsed % 60)) 133 | print('Best val Acc: {:4f}'.format(best_acc)) 134 | # 网络导入最好的网络权重 135 | model.load_state_dict(best_model_wts) 136 | return model 137 | 138 | # 微调网络 139 | if __name__ == '__main__': 140 | 141 | # 导入Pytorch中自带的resnet18网络模型 142 | model_ft = models.resnet18(pretrained=True) 143 | # 将网络模型的各层的梯度更新置为False 144 | for param in model_ft.parameters(): 145 | param.requires_grad = False 146 | 147 | # 修改网络模型的最后一个全连接层 148 | # 获取最后一个全连接层的输入通道数 149 | num_ftrs = model_ft.fc.in_features 150 | # 修改最后一个全连接层的的输出数为2 151 | model_ft.fc = nn.Linear(num_ftrs, 2) 152 | # 是否使用gpu 153 | if use_gpu: 154 | model_ft = model_ft.cuda() 155 | 156 | # 定义网络模型的损失函数 157 | criterion = nn.CrossEntropyLoss() 158 | 159 | # 只训练最后一个层 160 | # 采用随机梯度下降的方式，来优化网络模型 161 | optimizer_ft = torch.optim.SGD(model_ft.fc.parameters(), lr=0.001, momentum=0.9) 162 | 163 | # 定义学习率的更新方式，每5个epoch修改一次学习率 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https://raw.githubusercontent.com/Sun-DongYang/Pytorch/72d58ddfce58d1cfa5fb208aed31b6dc67e6759a/data/mnist/raw/train-labels-idx1-ubyte -------------------------------------------------------------------------------- /modelTrainingVisualization/logger.py: -------------------------------------------------------------------------------- 1 | # Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514 2 | import tensorflow as tf 3 | import numpy as np 4 | import scipy.misc 5 | try: 6 | from StringIO import StringIO # Python 2.7 7 | except ImportError: 8 | from io import BytesIO # Python 3.x 9 | 10 | 11 | class Logger(object): 12 | 13 | def __init__(self, log_dir): 14 | """Create a summary writer logging to log_dir.""" 15 | self.writer = tf.summary.FileWriter(log_dir) 16 | 17 | def scalar_summary(self, tag, value, step): 18 | """Log a scalar variable.""" 19 | summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)]) 20 | self.writer.add_summary(summary, step) 21 | 22 | def image_summary(self, tag, images, step): 23 | """Log a list of images.""" 24 | 25 | img_summaries = [] 26 | for i, img in enumerate(images): 27 | # Write the image to a string 28 | try: 29 | s = StringIO() 30 | except: 31 | s = BytesIO() 32 | scipy.misc.toimage(img).save(s, format="png") 33 | 34 | # Create an Image object 35 | img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(), 36 | height=img.shape[0], 37 | width=img.shape[1]) 38 | # Create a Summary value 39 | img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum)) 40 | 41 | # Create and write Summary 42 | summary = tf.Summary(value=img_summaries) 43 | self.writer.add_summary(summary, step) 44 | 45 | def histo_summary(self, tag, values, step, bins=1000): 46 | """Log a histogram of the tensor of values.""" 47 | 48 | # Create a histogram using numpy 49 | counts, bin_edges = np.histogram(values, bins=bins) 50 | 51 | # Fill the fields of the histogram proto 52 | hist = tf.HistogramProto() 53 | hist.min = float(np.min(values)) 54 | hist.max = float(np.max(values)) 55 | hist.num = int(np.prod(values.shape)) 56 | hist.sum = float(np.sum(values)) 57 | hist.sum_squares = float(np.sum(values**2)) 58 | 59 | # Drop the start of the first bin 60 | bin_edges = bin_edges[1:] 61 | 62 | # Add bin edges and counts 63 | for edge in bin_edges: 64 | hist.bucket_limit.append(edge) 65 | for c in counts: 66 | hist.bucket.append(c) 67 | 68 | # Create and write Summary 69 | summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)]) 70 | self.writer.add_summary(summary, step) 71 | self.writer.flush() -------------------------------------------------------------------------------- /modelTrainingVisualization/main.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from PIL import Image 3 | from torch.utils.data import Dataset, DataLoader 4 | import torch 5 | import torch.nn as nn 6 | from torch.optim import lr_scheduler 7 | from torch.autograd import Variable 8 | from torchvision import datasets, models, transforms 9 | import time 10 | import os 11 | # 导入用于记录训练过程的日志类 12 | from logger import Logger 13 | 14 | # 是否使用gpu运算 15 | use_gpu = torch.cuda.is_available() 16 | 17 | # 定义数据的处理方式 18 | data_transforms = { 19 | 'train': transforms.Compose([ 20 | # 将图像进行缩放，缩放为256*256 21 | transforms.Resize(256), 22 | # 在256*256的图像上随机裁剪出227*227大小的图像用于训练 23 | transforms.RandomResizedCrop(227), 24 | # 图像用于翻转 25 | transforms.RandomHorizontalFlip(), 26 | # 转换成tensor向量 27 | transforms.ToTensor(), 28 | # 对图像进行归一化操作 29 | # [0.485, 0.456, 0.406]，RGB通道的均值与标准差 30 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 31 | ]), 32 | # 测试集需要中心裁剪，甚至不裁剪，直接缩放为224*224for，不需要翻转 33 | 'val': transforms.Compose([ 34 | transforms.Resize(256), 35 | transforms.CenterCrop(227), 36 | transforms.ToTensor(), 37 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 38 | ]), 39 | } 40 | 41 | 42 | # 定义数据读入 43 | def Load_Image_Information(path): 44 | # 图像存储路径 45 | image_Root_Dir = r'图像文件夹路径' 46 | # 获取图像的路径 47 | iamge_Dir = os.path.join(image_Root_Dir, path) 48 | # 以RGB格式打开图像 49 | # Pytorch DataLoader就是使用PIL所读取的图像格式 50 | # 建议就用这种方法读取图像，当读入灰度图像时convert('') 51 | return Image.open(iamge_Dir).convert('RGB') 52 | 53 | 54 | # 定义自己数据集的数据读入类 55 | class my_Data_Set(nn.Module): 56 | def __init__(self, txt, transform=None, target_transform=None, loader=None): 57 | super(my_Data_Set, self).__init__() 58 | # 打开存储图像名与标签的txt文件 59 | fp = open(txt, 'r') 60 | images = [] 61 | labels = [] 62 | # 将图像名和图像标签对应存储起来 63 | for line in fp: 64 | line.strip('\n') 65 | line.rstrip() 66 | information = line.split() 67 | images.append(information[0]) 68 | labels.append(int(information[1])) 69 | self.images = images 70 | self.labels = labels 71 | self.transform = transform 72 | self.target_transform = target_transform 73 | self.loader = loader 74 | 75 | # 重写这个函数用来进行图像数据的读取 76 | def __getitem__(self, item): 77 | # 获取图像名和标签 78 | imageName = self.images[item] 79 | label = self.labels[item] 80 | # 读入图像信息 81 | image = self.loader(imageName) 82 | # 处理图像数据 83 | if self.transform is not None: 84 | image = self.transform(image) 85 | return image, label 86 | 87 | # 重写这个函数，来看数据集中含有多少数据 88 | def __len__(self): 89 | return len(self.images) 90 | 91 | 92 | # 生成Pytorch所需的DataLoader数据输入格式 93 | train_Data = my_Data_Set(r'train.txt路径', transform=data_transforms['train'], 94 | loader=Load_Image_Information) 95 | val_Data = my_Data_Set(r'val.txt路径', transform=data_transforms['val'], 96 | loader=Load_Image_Information) 97 | train_DataLoader = DataLoader(train_Data, batch_size=10, shuffle=True) 98 | val_DataLoader = DataLoader(val_Data, batch_size=10) 99 | dataloaders = {'train': train_DataLoader, 'val': val_DataLoader} 100 | # 读取数据集大小 101 | dataset_sizes = {'train': train_Data.__len__(), 'val': val_Data.__len__()} 102 | 103 | # 初始化路径来记录模型训练过程中训练阶段与验证阶段的loss变化 104 | # 训练阶段的日志文件存储路径 105 | train_log_path = r"./log/train_log" 106 | train_logger = Logger(train_log_path) 107 | # 验证阶段日志文件存储路径 108 | val_log_path = r"./log/val_log" 109 | val_logger = Logger(val_log_path) 110 | 111 | # 训练与验证网络（所有层都参加训练） 112 | def train_model(model, criterion, optimizer, scheduler, num_epochs=25): 113 | since = time.time() 114 | for epoch in range(num_epochs): 115 | print('Epoch {}/{}'.format(epoch, num_epochs - 1)) 116 | print('-' * 10) 117 | 118 | # 每训练一个epoch，验证一下网络模型 119 | for phase in ['train', 'val']: 120 | running_loss = 0.0 121 | running_corrects = 0.0 122 | if phase == 'train': 123 | # 学习率更新方式 124 | scheduler.step() 125 | # 调用模型训练 126 | model.train() 127 | # 依次获取所有图像，参与模型训练或测试 128 | for data in dataloaders[phase]: 129 | # 获取输入 130 | inputs, labels = data 131 | # 判断是否使用gpu 132 | if use_gpu: 133 | inputs = inputs.cuda() 134 | labels = labels.cuda() 135 | 136 | inputs, labels = Variable(inputs), Variable(labels) 137 | 138 | # 梯度清零 139 | optimizer.zero_grad() 140 | 141 | # 网络前向运行 142 | outputs = model(inputs) 143 | # 获取模型预测结果 144 | _, preds = torch.max(outputs.data, 1) 145 | # 计算Loss值 146 | loss = criterion(outputs, labels) 147 | # 反传梯度 148 | loss.backward() 149 | # 更新权重 150 | optimizer.step() 151 | # 计算一个epoch的loss值 152 | running_loss += loss.item() * inputs.size(0) 153 | # 计算一个epoch的准确率 154 | running_corrects += torch.sum(preds == labels.data) 155 | # 计算Loss和准确率的均值 156 | epoch_loss = running_loss / dataset_sizes[phase] 157 | epoch_acc = float(running_corrects) / dataset_sizes[phase] 158 | print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc)) 159 | torch.save(model.state_dict(), 'The_' + str(epoch) + '_epoch_model.pkl') 160 | 161 | # 1. 记录这个epoch的loss值和准确率 162 | info = {'loss': epoch_loss, 'accuracy': epoch_acc} 163 | for tag, value in info.items(): 164 | train_logger.scalar_summary(tag, value, epoch) 165 | 166 | # 2. 记录这个epoch的模型的参数和梯度 167 | for tag, value in model.named_parameters(): 168 | tag = tag.replace('.', '/') 169 | train_logger.histo_summary(tag, value.data.cpu().numpy(), epoch) 170 | train_logger.histo_summary(tag + '/grad', value.grad.data.cpu().numpy(), epoch) 171 | 172 | # 3. 记录最后一个epoch的图像 173 | info = {'images': inputs.cpu().numpy()} 174 | 175 | for tag, images in info.items(): 176 | train_logger.image_summary(tag, images, epoch) 177 | 178 | else: 179 | # 取消验证阶段的梯度 180 | with torch.no_grad(): 181 | # 调用模型测试 182 | model.eval() 183 | # 依次获取所有图像，参与模型训练或测试 184 | for data in dataloaders[phase]: 185 | # 获取输入 186 | inputs, labels = data 187 | # 判断是否使用gpu 188 | if use_gpu: 189 | inputs = inputs.cuda() 190 | labels = labels.cuda() 191 | 192 | inputs, labels = Variable(inputs), Variable(labels) 193 | 194 | # 网络前向运行 195 | outputs = model(inputs) 196 | _, preds = torch.max(outputs.data, 1) 197 | # 计算Loss值 198 | loss = criterion(outputs, labels) 199 | # 计算一个epoch的loss值 200 | running_loss += loss.item() * inputs.size(0) 201 | # 计算一个epoch的准确率 202 | running_corrects += torch.sum(preds == labels.data) 203 | 204 | # 计算Loss和准确率的均值 205 | epoch_loss = running_loss / dataset_sizes[phase] 206 | epoch_acc = float(running_corrects) / dataset_sizes[phase] 207 | print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc)) 208 | # 1. 记录这个epoch的loss值和准确率 209 | info = {'loss': epoch_loss, 'accuracy': epoch_acc} 210 | for tag, value in info.items(): 211 | val_logger.scalar_summary(tag, value, epoch) 212 | 213 | # 2. 记录这个epoch的模型的参数和梯度 214 | for tag, value in model.named_parameters(): 215 | tag = tag.replace('.', '/') 216 | val_logger.histo_summary(tag, value.data.cpu().numpy(), epoch) 217 | val_logger.histo_summary(tag + '/grad', value.grad.data.cpu().numpy(), epoch) 218 | 219 | # 3. 记录最后一个epoch的图像 220 | info = {'images': inputs.cpu().numpy()} 221 | for tag, images in info.items(): 222 | val_logger.image_summary(tag, images, epoch) 223 | 224 | time_elapsed = time.time() - since 225 | print('Training complete in {:.0f}m {:.0f}s'.format( 226 | time_elapsed // 60, time_elapsed % 60)) 227 | 228 | 229 | # 精调AlexNet 230 | if __name__ == '__main__': 231 | 232 | # 导入Pytorch封装的AlexNet网络模型 233 | model = models.alexnet(pretrained=True) 234 | # 获取最后一个全连接层的输入通道数 235 | num_input = model.classifier[6].in_features 236 | # 获取全连接层的网络结构 237 | feature_model = list(model.classifier.children()) 238 | # 去掉原来的最后一层 239 | feature_model.pop() 240 | # 添加上适用于自己数据集的全连接层 241 | feature_model.append(nn.Linear(num_input, 260)) 242 | # 仿照这里的方法，可以修改网络的结构，不仅可以修改最后一个全连接层 243 | # 还可以为网络添加新的层 244 | # 重新生成网络的后半部分 245 | model.classifier = nn.Sequential(*feature_model) 246 | if use_gpu: 247 | model = model.cuda() 248 | # 定义损失函数 249 | criterion = nn.CrossEntropyLoss() 250 | 251 | # 为不同层设定不同的学习率 252 | fc_params = list(map(id, model.classifier[6].parameters())) 253 | base_params = filter(lambda p: id(p) not in fc_params, model.parameters()) 254 | params = [{"params": base_params, "lr": 0.0001}, 255 | {"params": model.classifier[6].parameters(), "lr": 0.001}, ] 256 | optimizer_ft = torch.optim.SGD(params, momentum=0.9) 257 | 258 | # 定义学习率的更新方式，每5个epoch修改一次学习率 259 | exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=5, gamma=0.1) 260 | train_model(model, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=10) 261 | 262 | -------------------------------------------------------------------------------- /multiLabel/multiLabel.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | from torch.utils.data import Dataset, DataLoader 3 | import torch 4 | import torch.nn as nn 5 | from torch.optim import lr_scheduler 6 | from torchvision import datasets, models, transforms 7 | import time 8 | import os 9 | 10 | # 是否使用gpu运算 11 | use_gpu = torch.cuda.is_available() 12 | 13 | # 定义数据的处理方式 14 | data_transforms = { 15 | 'train': transforms.Compose([ 16 | # 将图像进行缩放，缩放为256*256 17 | transforms.Resize(256), 18 | # 在256*256的图像上随机裁剪出227*227大小的图像用于训练 19 | transforms.RandomResizedCrop(227), 20 | # 图像用于翻转 21 | transforms.RandomHorizontalFlip(), 22 | # 转换成tensor向量 23 | transforms.ToTensor(), 24 | # 对图像进行归一化操作 25 | # [0.485, 0.456, 0.406]，RGB通道的均值与标准差 26 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 27 | ]), 28 | # 测试集需要中心裁剪，甚至不裁剪，直接缩放为224*224for，不需要翻转 29 | 'val': transforms.Compose([ 30 | transforms.Resize(256), 31 | transforms.CenterCrop(227), 32 | transforms.ToTensor(), 33 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 34 | ]), 35 | } 36 | 37 | 38 | # 定义数据读入 39 | def Load_Image_Information(path): 40 | # 图像存储路径 41 | image_Root_Dir = r'图像文件夹路径' 42 | # 获取图像的路径 43 | iamge_Dir = os.path.join(image_Root_Dir, path) 44 | # 以RGB格式打开图像 45 | # Pytorch DataLoader就是使用PIL所读取的图像格式 46 | # 建议就用这种方法读取图像，当读入灰度图像时convert('') 47 | return Image.open(iamge_Dir).convert('RGB') 48 | 49 | 50 | # 定义自己数据集的数据读入类 51 | class my_Data_Set(nn.Module): 52 | def __init__(self, txt, transform=None, target_transform=None, loader=None): 53 | super(my_Data_Set, self).__init__() 54 | # 打开存储图像名与标签的txt文件 55 | fp = open(txt, 'r') 56 | images = [] 57 | labels = [] 58 | # 将图像名和图像标签对应存储起来 59 | for line in fp: 60 | line.strip('\n') 61 | line.rstrip() 62 | information = line.split() 63 | images.append(information[0]) 64 | # 将标签信息由str类型转换为float类型 65 | labels.append([float(l) for l in information[1:len(information)]]) 66 | self.images = images 67 | self.labels = labels 68 | self.transform = transform 69 | self.target_transform = target_transform 70 | self.loader = loader 71 | 72 | # 重写这个函数用来进行图像数据的读取 73 | def __getitem__(self, item): 74 | # 获取图像名和标签 75 | imageName = self.images[item] 76 | label = self.labels[item] 77 | # 读入图像信息 78 | image = self.loader(imageName) 79 | # 处理图像数据 80 | if self.transform is not None: 81 | image = self.transform(image) 82 | # 需要将标签转换为float类型，BCELoss只接受float类型 83 | label = torch.FloatTensor(label) 84 | return image, label 85 | 86 | # 重写这个函数，来看数据集中含有多少数据 87 | def __len__(self): 88 | return len(self.images) 89 | 90 | 91 | # 生成Pytorch所需的DataLoader数据输入格式 92 | train_Data = my_Data_Set(r'train.txt路径', transform=data_transforms['train'], loader=Load_Image_Information) 93 | val_Data = my_Data_Set(r'val.txt路径', transform=data_transforms['val'], loader=Load_Image_Information) 94 | train_DataLoader = DataLoader(train_Data, batch_size=10, shuffle=True) 95 | val_DataLoader = DataLoader(val_Data, batch_size=10) 96 | dataloaders = {'train':train_DataLoader, 'val':val_DataLoader} 97 | # 读取数据集大小 98 | dataset_sizes = {'train': train_Data.__len__(), 'val': val_Data.__len__()} 99 | 100 | # 训练与验证网络（所有层都参加训练） 101 | def train_model(model, criterion, optimizer, scheduler, num_epochs=25): 102 | Sigmoid_fun = nn.Sigmoid() 103 | since = time.time() 104 | 105 | for epoch in range(num_epochs): 106 | print('Epoch {}/{}'.format(epoch, num_epochs - 1)) 107 | print('-' * 10) 108 | 109 | # 每训练一个epoch，验证一下网络模型 110 | for phase in ['train', 'val']: 111 | running_loss = 0.0 112 | running_precision = 0.0 113 | running_recall = 0.0 114 | batch_num = 0 115 | 116 | if phase == 'train': 117 | # 学习率更新方式 118 | scheduler.step() 119 | # 调用模型训练 120 | model.train() 121 | 122 | # 依次获取所有图像，参与模型训练或测试 123 | for data in dataloaders[phase]: 124 | # 获取输入 125 | inputs, labels = data 126 | # 判断是否使用gpu 127 | if use_gpu: 128 | inputs = inputs.cuda() 129 | labels = labels.cuda() 130 | 131 | # 梯度清零 132 | optimizer.zero_grad() 133 | 134 | # 网络前向运行 135 | outputs = model(inputs) 136 | # 计算Loss值 137 | loss = criterion(Sigmoid_fun(outputs), labels) 138 | 139 | # 这里根据自己的需求选择模型预测结果准确率的函数 140 | precision, recall = calculate_acuracy_mode_one(Sigmoid_fun(outputs), labels) 141 | # precision, recall = calculate_acuracy_mode_two(Sigmoid_fun(outputs), labels) 142 | running_precision += precision 143 | running_recall += recall 144 | batch_num += 1 145 | # 反传梯度 146 | loss.backward() 147 | # 更新权重 148 | optimizer.step() 149 | # 计算一个epoch的loss值和准确率 150 | running_loss += loss.item() * inputs.size(0) 151 | else: 152 | # 取消验证阶段的梯度 153 | with torch.no_grad(): 154 | # 调用模型测试 155 | model.eval() 156 | # 依次获取所有图像，参与模型训练或测试 157 | for data in dataloaders[phase]: 158 | # 获取输入 159 | inputs, labels = data 160 | # 判断是否使用gpu 161 | if use_gpu: 162 | inputs = inputs.cuda() 163 | labels = labels.cuda() 164 | 165 | # 网络前向运行 166 | outputs = model(inputs) 167 | # 计算Loss值 168 | # BCELoss的输入（1、网络模型的输出必须经过sigmoid；2、标签必须是float类型的tensor） 169 | loss = criterion(Sigmoid_fun(outputs), labels) 170 | # 计算一个epoch的loss值和准确率 171 | running_loss += loss.item() * inputs.size(0) 172 | 173 | # 这里根据自己的需求选择模型预测结果准确率的函数 174 | precision, recall = calculate_acuracy_mode_one(Sigmoid_fun(outputs), labels) 175 | # precision, recall = calculate_acuracy_mode_two(Sigmoid_fun(outputs), labels) 176 | running_precision += precision 177 | running_recall += recall 178 | batch_num += 1 179 | 180 | # 计算Loss和准确率的均值 181 | epoch_loss = running_loss / dataset_sizes[phase] 182 | print('{} Loss: {:.4f} '.format(phase, epoch_loss)) 183 | epoch_precision = running_precision / batch_num 184 | print('{} Precision: {:.4f} '.format(phase, epoch_precision)) 185 | epoch_recall = running_recall / batch_num 186 | print('{} Recall: {:.4f} '.format(phase, epoch_recall)) 187 | torch.save(model.state_dict(),'The_'+ str(epoch) + '_epoch_model.pkl'"Themodel_AlexNet.pkl") 188 | time_elapsed = time.time() - since 189 | print('Training complete in {:.0f}m {:.0f}s'.format( 190 | time_elapsed // 60, time_elapsed % 60)) 191 | 192 | # 计算准确率——方式1 193 | # 设定一个阈值，当预测的概率值大于这个阈值，则认为这幅图像中含有这类标签 194 | def calculate_acuracy_mode_one(model_pred, labels): 195 | # 注意这里的model_pred是经过sigmoid处理的，sigmoid处理后可以视为预测是这一类的概率 196 | # 预测结果，大于这个阈值则视为预测正确 197 | accuracy_th = 0.5 198 | pred_result = model_pred > accuracy_th 199 | pred_result = pred_result.float() 200 | pred_one_num = torch.sum(pred_result) 201 | if pred_one_num == 0: 202 | return 0, 0 203 | target_one_num = torch.sum(labels) 204 | true_predict_num = torch.sum(pred_result * labels) 205 | # 模型预测的结果中有多少个是正确的 206 | precision = true_predict_num / pred_one_num 207 | # 模型预测正确的结果中，占所有真实标签的数量 208 | recall = true_predict_num / target_one_num 209 | 210 | return precision.item(), recall.item() 211 | 212 | # 计算准确率——方式2 213 | # 取预测概率最大的前top个标签，作为模型的预测结果 214 | def calculate_acuracy_mode_two(model_pred, labels): 215 | # 取前top个预测结果作为模型的预测结果 216 | precision = 0 217 | recall = 0 218 | top = 5 219 | # 对预测结果进行按概率值进行降序排列，取概率最大的top个结果作为模型的预测结果 220 | pred_label_locate = torch.argsort(model_pred, descending=True)[:, 0:top] 221 | for i in range(model_pred.shape[0]): 222 | temp_label = torch.zeros(1, model_pred.shape[1]) 223 | temp_label[0,pred_label_locate[i]] = 1 224 | target_one_num = torch.sum(labels[i]) 225 | true_predict_num = torch.sum(temp_label * labels[i]) 226 | # 对每一幅图像进行预测准确率的计算 227 | precision += true_predict_num / top 228 | # 对每一幅图像进行预测查全率的计算 229 | recall += true_predict_num / target_one_num 230 | return precision, recall 231 | # 精调AlexNet 232 | if __name__ == '__main__': 233 | 234 | # 导入Pytorch封装的AlexNet网络模型 235 | model = models.alexnet(pretrained=True) 236 | # 获取最后一个全连接层的输入通道数 237 | num_input = model.classifier[6].in_features 238 | # 获取全连接层的网络结构 239 | feature_model = list(model.classifier.children()) 240 | # 去掉原来的最后一层 241 | feature_model.pop() 242 | # 添加上适用于自己数据集的全连接层 243 | # 260数据集的类别数 244 | feature_model.append(nn.Linear(num_input, 260)) 245 | # 仿照这里的方法，可以修改网络的结构，不仅可以修改最后一个全连接层 246 | # 还可以为网络添加新的层 247 | # 重新生成网络的后半部分 248 | model.classifier = nn.Sequential(*feature_model) 249 | if use_gpu: 250 | model = model.cuda() 251 | # 定义损失函数 252 | criterion = nn.BCELoss() 253 | 254 | # 为不同层设定不同的学习率 255 | fc_params = list(map(id, model.classifier[6].parameters())) 256 | base_params = filter(lambda p: id(p) not in fc_params, model.parameters()) 257 | params = [{"params": base_params, "lr":0.0001}, 258 | {"params": model.classifier[6].parameters(), "lr":0.001},] 259 | optimizer_ft = torch.optim.SGD(params, momentum=0.9) 260 | 261 | # 定义学习率的更新方式，每5个epoch修改一次学习率 262 | exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=5, gamma=0.1) 263 | train_model(model, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=10) 264 | 265 | -------------------------------------------------------------------------------- /viewFeatureMap/husky.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Sun-DongYang/Pytorch/72d58ddfce58d1cfa5fb208aed31b6dc67e6759a/viewFeatureMap/husky.png -------------------------------------------------------------------------------- /viewFeatureMap/viewAlexNetFeatureMap.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torchvision import models, transforms 3 | from PIL import Image 4 | import matplotlib.pyplot as plt 5 | import numpy as np 6 | import scipy.misc 7 | 8 | 9 | # 导入数据 10 | def get_image_info(image_dir): 11 | # 以RGB格式打开图像 12 | # Pytorch DataLoader就是使用PIL所读取的图像格式 13 | # 建议就用这种方法读取图像，当读入灰度图像时convert('') 14 | image_info = Image.open(image_dir).convert('RGB') 15 | # 数据预处理方法 16 | image_transform = transforms.Compose([ 17 | transforms.Resize(256), 18 | transforms.CenterCrop(224), 19 | transforms.ToTensor(), 20 | transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 21 | ]) 22 | image_info = image_transform(image_info) 23 | image_info = image_info.unsqueeze(0) 24 | return image_info 25 | 26 | # 获取第k层的特征图 27 | def get_k_layer_feature_map(feature_extractor, k, x): 28 | with torch.no_grad(): 29 | for index,layer in enumerate(feature_extractor): 30 | x = layer(x) 31 | if k == index: 32 | return x 33 | 34 | # 可视化特征图 35 | def show_feature_map(feature_map): 36 | feature_map = feature_map.squeeze(0) 37 | feature_map = feature_map.cpu().numpy() 38 | feature_map_num = feature_map.shape[0] 39 | row_num = np.ceil(np.sqrt(feature_map_num)) 40 | plt.figure() 41 | for index in range(1, feature_map_num+1): 42 | plt.subplot(row_num, row_num, index) 43 | plt.imshow(feature_map[index-1], cmap='gray') 44 | plt.axis('off') 45 | scipy.misc.imsave(str(index)+".png", feature_map[index-1]) 46 | plt.show() 47 | 48 | 49 | 50 | 51 | if __name__ == '__main__': 52 | # 初始化图像的路径 53 | image_dir = r"husky.png" 54 | # 定义提取第几层的feature map 55 | k = 1 56 | # 导入Pytorch封装的AlexNet网络模型 57 | model = models.alexnet(pretrained=True) 58 | # 是否使用gpu运算 59 | use_gpu = torch.cuda.is_available() 60 | use_gpu =False 61 | # 读取图像信息 62 | image_info = get_image_info(image_dir) 63 | # 判断是否使用gpu 64 | if use_gpu: 65 | model = model.cuda() 66 | image_info = image_info.cuda() 67 | # alexnet只有features部分有特征图 68 | # classifier部分的feature map是向量 69 | feature_extractor = model.features 70 | feature_map = get_k_layer_feature_map(feature_extractor, k, image_info) 71 | show_feature_map(feature_map) 72 | 73 | --------------------------------------------------------------------------------