fix: add figures and data of ResNet 50/152 & MobileNetV2

mzh/new_mzh/ResNet_nobias/ptq_result_ResNet152.txt

 title_list:
 INT_2 INT_3 INT_4 INT_5 INT_6 INT_7 INT_8 INT_9 INT_10 INT_11 INT_12 INT_13 INT_14 INT_15 INT_16 POT_2 POT_3 POT_4 POT_5 POT_6 POT_7 POT_8 FLOAT_3_E1 FLOAT_4_E1 FLOAT_4_E2 FLOAT_5_E1 FLOAT_5_E2 FLOAT_5_E3 FLOAT_6_E1 FLOAT_6_E2 FLOAT_6_E3 FLOAT_6_E4 FLOAT_7_E1 FLOAT_7_E2 FLOAT_7_E3 FLOAT_7_E4 FLOAT_7_E5 FLOAT_8_E1 FLOAT_8_E2 FLOAT_8_E3 FLOAT_8_E4 FLOAT_8_E5 FLOAT_8_E6
 js_flops_list:
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 js_param_list:
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+20.85553941977874 9.981209988545288 1.3539305429512496 0.30139434372520046 0.07038894495212882 0.016983314589243823 0.004172975453783345 0.0010420360112590532 0.00026273648166581423 7.17926315777753e-05 2.4605053810581976e-05 1.317531321555632e-05 1.9623210180810698e-05 1.747944047326041e-05 1.170199482974471e-05 17.390004975267495 3.8124967157643033 1.0178092545455621 1.0125712143846217 0.9928878407387453 1.0125745208871095 0.995760463684502 3.5930026564803716 1.1991610585566574 0.6596486829871696 0.6274086902313186 0.2277369707300968 0.20674715542055963 0.45375203785887974 0.11819179575270293 0.05360085504661807 0.20648789904506443 0.3899066809035698 0.0837931514468961 0.013553482490574238 0.05351237083520507 0.206488208407236 0.3632293653938247 0.07115836515515826 0.003443783278898041 0.01349690316709514 0.0535122559122496 0.20908980476129443
 ptq_acc_list:
-10.0 10.01 11.68 35.86 71.22 88.15 90.01 90.61 90.77 90.77 90.79 90.77 90.77 90.77 90.77 10.0 8.88 18.15 23.5 19.47 15.55 15.61 12.42 18.8 37.09 19.94 44.44 74.39 40.94 49.59 88.61 75.76 46.23 56.11 90.24 88.38 78.55 56.06 53.84 90.71 90.27 88.32 71.82
+10.0 10.0 11.86 46.5 75.15 88.16 90.38 90.67 90.81 90.78 90.81 90.82 90.79 90.77 90.77 10.0 10.32 16.21 18.59 17.49 17.34 18.47 13.0 10.63 33.64 21.79 46.89 72.72 36.71 68.56 88.48 76.4 52.7 58.52 90.32 88.99 75.24 51.23 57.34 90.59 90.41 88.78 70.99
 acc_loss_list:
-0.8898314421064228 0.8897212735485291 0.8713231243803019 0.6049355513936322 0.21537953068194335 0.028864162168117113 0.008372810399911765 0.0017626969262971974 0.0 0.0 -0.0002203371157872671 0.0 0.0 0.0 0.0 0.8898314421064228 0.9021703205905035 0.8000440674231575 0.7411038889500936 0.7855018177812052 0.8286878924754875 0.8280268811281261 0.8631706510961771 0.7928831111600749 0.5913848187727222 0.7803238955602071 0.5104109287209431 0.18045609782967936 0.548969923983695 0.45367412140575075 0.023796408505012634 0.16536300539825924 0.49069075685799274 0.38184422165913845 0.005838933568359603 0.02633028533656495 0.1346259777459513 0.3823950644486063 0.4068524843009804 0.0006610113473614881 0.0055084278946788585 0.02699129668392644 0.20876941720832878
+0.8898314421064228 0.8898314421064228 0.8693400903382175 0.48771620579486613 0.17208328742976745 0.028753993610223638 0.004296573757849516 0.001101685578935709 -0.0004406742315743776 -0.00011016855789363355 -0.0004406742315743776 -0.0005508427894678545 -0.0002203371157872671 0.0 0.0 0.8898314421064228 0.8863060482538283 0.8214167676545114 0.79519665087584 0.8073151922441336 0.8089677206125372 0.7965186735705629 0.8567808747383496 0.8828908229591275 0.6293929712460063 0.7599427123498953 0.4834196320370166 0.19885424699790677 0.5955712239726781 0.24468436708163485 0.025228599757629085 0.1583122176930703 0.41941169990084826 0.3552935992067863 0.0049575851052110044 0.01961000330505675 0.17109177040872536 0.43560647791120416 0.36829348903822845 0.0019830340420843077 0.003966068084168772 0.021923543020821803 0.21791340751349567

mzh/new_mzh/ResNet_nobias/ptq_result_ResNet50.txt

 title_list:
 INT_2 INT_3 INT_4 INT_5 INT_6 INT_7 INT_8 INT_9 INT_10 INT_11 INT_12 INT_13 INT_14 INT_15 INT_16 POT_2 POT_3 POT_4 POT_5 POT_6 POT_7 POT_8 FLOAT_3_E1 FLOAT_4_E1 FLOAT_4_E2 FLOAT_5_E1 FLOAT_5_E2 FLOAT_5_E3 FLOAT_6_E1 FLOAT_6_E2 FLOAT_6_E3 FLOAT_6_E4 FLOAT_7_E1 FLOAT_7_E2 FLOAT_7_E3 FLOAT_7_E4 FLOAT_7_E5 FLOAT_8_E1 FLOAT_8_E2 FLOAT_8_E3 FLOAT_8_E4 FLOAT_8_E5 FLOAT_8_E6
 js_flops_list:
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 js_param_list:
-2428.388695366032 1576.3835862311166 735.7144816775965 330.7879670826985 153.8651132826839 73.29165336554594 35.886440151847374 18.054417795113007 8.71550268107359 4.208844604961609 2.089532093278776 0.9633847051060299 0.3967191667447389 0.20409221634042343 0.09841711129863948 2428.32641725148 1252.5298934046723 88.6695229301097 18.02081222203601 17.817638699939312 17.81805197224828 17.817443494485715 883.6762566395342 364.86096182524466 232.53743290664556 164.79476403956693 96.11400180562215 16.67957044460877 78.7486628351845 46.083739584063444 6.189988926373023 4.988900305675509 39.98449203615441 24.734066963325354 2.585781231886643 1.3054649551746371 4.9472935954404 21.66781080710387 14.319081489805278 1.0990111398076245 0.33141428747561985 1.2786737197199143 5.037454816228963
+33.721554105756816 13.269154166033433 1.877153395798168 0.4076554546710941 0.09554313376446293 0.023148392970637945 0.005675039691374743 0.001426682819835179 0.00036345209532231146 9.38985449811094e-05 2.837714142247516e-05 3.191036192167962e-05 2.277355467755515e-05 1.0829856218760036e-05 8.09611746277672e-06 33.383130206500184 5.912517637356709 1.725167775811526 1.6936075456198305 1.7079693950603978 1.745066474153093 1.7771297088256834 6.363561522947833 2.233996327924317 1.0358764351056118 1.245546534684258 0.3518593191648724 0.3809527745802683 0.9287923064091098 0.17743315049465594 0.09900998360709243 0.38070531858108136 0.8063356331707637 0.12348247277252783 0.024949418674407576 0.09893290024757509 0.3807042031089577 0.7535293303127147 0.1040074579869313 0.006337410559870987 0.02488997718210982 0.09893348151517127 0.4220865848581757
 ptq_acc_list:
-10.0 10.0 20.08 75.03 88.19 89.81 90.23 90.5 90.48 90.54 90.53 90.57 90.59 90.57 90.56 10.0 12.24 16.16 13.0 21.38 19.87 21.15 12.25 12.99 37.39 24.11 76.45 76.57 38.86 85.05 88.16 78.1 51.66 86.67 90.09 87.89 76.06 54.48 87.17 90.32 90.06 87.96 79.95
+10.0 10.03 14.16 73.84 88.41 89.87 90.3 90.48 90.45 90.54 90.53 90.54 90.58 90.57 90.56 10.0 10.93 22.69 23.64 17.97 24.65 23.01 15.05 15.76 45.01 26.09 75.66 75.82 41.03 84.68 87.95 78.47 49.61 86.32 90.14 88.01 77.2 54.96 87.52 90.17 90.02 88.25 78.85
 acc_loss_list:
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mzh/new_mzh/ResNet_nobias/readme.md

 ## update: <br>2023.4.24<br>
+补充了一些数据和拟合图<br>
+尝试将ResNet18,ResNet50,ResNet152,MobileNetV2四个模型的数据点拟合在同一张图上，效果还不错。不过考虑到这四个模型的结构较为相似，暂不确定与其他的结构差异较大的模型的数据点在一起拟合效果如何。
+1. ResNet50:
+<img src = "fig/50_flops_f.png" class="h-90 auto">
+<img src = "fig/50_params_F.png" class="h-90 auto">
+2. ResNet152:
+<img src = "fig/152_flops_f.png" class="h-90 auto">
+<img src = "fig/152_params_f.png" class="h-90 auto">
+3. 综合了ResNet18,ResNet50,ResNet152,MobileNetV2的数据点的拟合图
+<img src = "fig/total_flops.png" class="h-90 auto">
+<img src = "fig/total_params.png" class="h-90 auto">
+## update: <br>2023.4.24<br>
 - 解决的问题：
 1. PoT拟合异常 <br>
 2. 曲线拟合效果较差