add ResNet weight dump
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colin
parent
7ab55557cc
commit
ef0dd5bc00
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@ -0,0 +1,143 @@
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int RN50_conv1_weight[]={0,9407,}
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int RN50_bn1_running_mean[]={9408,9471,}
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int RN50_bn1_running_var[]={9472,9535,}
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int RN50_layer1__modules_0_conv1_weight[]={9536,13631,}
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int RN50_layer1__modules_0_bn1_running_mean[]={13632,13695,}
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int RN50_layer1__modules_0_bn1_running_var[]={13696,13759,}
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int RN50_layer1__modules_0_conv2_weight[]={13760,50623,}
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int RN50_layer1__modules_0_bn2_running_mean[]={50624,50687,}
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int RN50_layer1__modules_0_bn2_running_var[]={50688,50751,}
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int RN50_layer1__modules_0_conv3_weight[]={50752,67135,}
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int RN50_layer1__modules_0_bn3_running_mean[]={67136,67391,}
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int RN50_layer1__modules_0_bn3_running_var[]={67392,67647,}
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int RN50_layer1__modules_0_downsample__modules_0_weight[]={67648,84031,}
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int RN50_layer1__modules_0_downsample__modules_1_running_mean[]={84032,84287,}
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int RN50_layer1__modules_0_downsample__modules_1_running_var[]={84288,84543,}
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int RN50_layer2__modules_0_conv1_weight[]={84544,117311,}
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int RN50_layer2__modules_0_bn1_running_mean[]={117312,117439,}
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int RN50_layer2__modules_0_bn1_running_var[]={117440,117567,}
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int RN50_layer2__modules_0_conv2_weight[]={117568,265023,}
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int RN50_layer2__modules_0_bn2_running_mean[]={265024,265151,}
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int RN50_layer2__modules_0_bn2_running_var[]={265152,265279,}
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int RN50_layer2__modules_0_conv3_weight[]={265280,330815,}
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int RN50_layer2__modules_0_bn3_running_mean[]={330816,331327,}
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int RN50_layer2__modules_0_bn3_running_var[]={331328,331839,}
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int RN50_layer2__modules_0_downsample__modules_0_weight[]={331840,462911,}
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int RN50_layer2__modules_0_downsample__modules_1_running_mean[]={462912,463423,}
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int RN50_layer2__modules_0_downsample__modules_1_running_var[]={463424,463935,}
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int RN50_layer2__modules_1_conv1_weight[]={463936,529471,}
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int RN50_layer2__modules_1_bn1_running_mean[]={529472,529599,}
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int RN50_layer2__modules_1_bn1_running_var[]={529600,529727,}
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int RN50_layer2__modules_1_conv2_weight[]={529728,677183,}
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int RN50_layer2__modules_1_bn2_running_mean[]={677184,677311,}
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int RN50_layer2__modules_1_bn2_running_var[]={677312,677439,}
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int RN50_layer2__modules_1_conv3_weight[]={677440,742975,}
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int RN50_layer2__modules_1_bn3_running_mean[]={742976,743487,}
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int RN50_layer2__modules_1_bn3_running_var[]={743488,743999,}
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int RN50_layer2__modules_2_conv1_weight[]={744000,809535,}
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int RN50_layer2__modules_2_bn1_running_mean[]={809536,809663,}
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int RN50_layer2__modules_2_bn1_running_var[]={809664,809791,}
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int RN50_layer2__modules_2_conv2_weight[]={809792,957247,}
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int RN50_layer2__modules_2_bn2_running_mean[]={957248,957375,}
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int RN50_layer2__modules_2_bn2_running_var[]={957376,957503,}
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int RN50_layer2__modules_2_conv3_weight[]={957504,1023039,}
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int RN50_layer2__modules_2_bn3_running_mean[]={1023040,1023551,}
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int RN50_layer2__modules_2_bn3_running_var[]={1023552,1024063,}
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int RN50_layer2__modules_3_conv1_weight[]={1024064,1089599,}
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int RN50_layer2__modules_3_bn1_running_mean[]={1089600,1089727,}
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int RN50_layer2__modules_3_bn1_running_var[]={1089728,1089855,}
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int RN50_layer2__modules_3_conv2_weight[]={1089856,1237311,}
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int RN50_layer2__modules_3_bn2_running_mean[]={1237312,1237439,}
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int RN50_layer2__modules_3_bn2_running_var[]={1237440,1237567,}
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int RN50_layer2__modules_3_conv3_weight[]={1237568,1303103,}
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int RN50_layer2__modules_3_bn3_running_mean[]={1303104,1303615,}
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int RN50_layer2__modules_3_bn3_running_var[]={1303616,1304127,}
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int RN50_layer3__modules_0_conv1_weight[]={1304128,1435199,}
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int RN50_layer3__modules_0_bn1_running_mean[]={1435200,1435455,}
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int RN50_layer3__modules_0_bn1_running_var[]={1435456,1435711,}
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int RN50_layer3__modules_0_conv2_weight[]={1435712,2025535,}
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int RN50_layer3__modules_0_bn2_running_mean[]={2025536,2025791,}
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int RN50_layer3__modules_0_bn2_running_var[]={2025792,2026047,}
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int RN50_layer3__modules_0_conv3_weight[]={2026048,2288191,}
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int RN50_layer3__modules_0_bn3_running_mean[]={2288192,2289215,}
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int RN50_layer3__modules_0_bn3_running_var[]={2289216,2290239,}
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int RN50_layer3__modules_0_downsample__modules_0_weight[]={2290240,2814527,}
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int RN50_layer3__modules_0_downsample__modules_1_running_mean[]={2814528,2815551,}
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int RN50_layer3__modules_0_downsample__modules_1_running_var[]={2815552,2816575,}
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int RN50_layer3__modules_1_conv1_weight[]={2816576,3078719,}
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int RN50_layer3__modules_1_bn1_running_mean[]={3078720,3078975,}
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int RN50_layer3__modules_1_bn1_running_var[]={3078976,3079231,}
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int RN50_layer3__modules_1_conv2_weight[]={3079232,3669055,}
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int RN50_layer3__modules_1_bn2_running_mean[]={3669056,3669311,}
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int RN50_layer3__modules_1_bn2_running_var[]={3669312,3669567,}
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int RN50_layer3__modules_1_conv3_weight[]={3669568,3931711,}
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int RN50_layer3__modules_1_bn3_running_mean[]={3931712,3932735,}
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int RN50_layer3__modules_1_bn3_running_var[]={3932736,3933759,}
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int RN50_layer3__modules_2_conv1_weight[]={3933760,4195903,}
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int RN50_layer3__modules_2_bn1_running_mean[]={4195904,4196159,}
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int RN50_layer3__modules_2_bn1_running_var[]={4196160,4196415,}
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int RN50_layer3__modules_2_conv2_weight[]={4196416,4786239,}
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int RN50_layer3__modules_2_bn2_running_mean[]={4786240,4786495,}
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int RN50_layer3__modules_2_bn2_running_var[]={4786496,4786751,}
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int RN50_layer3__modules_2_conv3_weight[]={4786752,5048895,}
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int RN50_layer3__modules_2_bn3_running_mean[]={5048896,5049919,}
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int RN50_layer3__modules_2_bn3_running_var[]={5049920,5050943,}
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int RN50_layer3__modules_3_conv1_weight[]={5050944,5313087,}
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int RN50_layer3__modules_3_bn1_running_mean[]={5313088,5313343,}
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int RN50_layer3__modules_3_bn1_running_var[]={5313344,5313599,}
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int RN50_layer3__modules_3_conv2_weight[]={5313600,5903423,}
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int RN50_layer3__modules_3_bn2_running_mean[]={5903424,5903679,}
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int RN50_layer3__modules_3_bn2_running_var[]={5903680,5903935,}
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int RN50_layer3__modules_3_conv3_weight[]={5903936,6166079,}
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int RN50_layer3__modules_3_bn3_running_mean[]={6166080,6167103,}
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int RN50_layer3__modules_3_bn3_running_var[]={6167104,6168127,}
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int RN50_layer3__modules_4_conv1_weight[]={6168128,6430271,}
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int RN50_layer3__modules_4_bn1_running_mean[]={6430272,6430527,}
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int RN50_layer3__modules_4_bn1_running_var[]={6430528,6430783,}
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int RN50_layer3__modules_4_conv2_weight[]={6430784,7020607,}
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int RN50_layer3__modules_4_bn2_running_mean[]={7020608,7020863,}
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int RN50_layer3__modules_4_bn2_running_var[]={7020864,7021119,}
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int RN50_layer3__modules_4_conv3_weight[]={7021120,7283263,}
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int RN50_layer3__modules_4_bn3_running_mean[]={7283264,7284287,}
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int RN50_layer3__modules_4_bn3_running_var[]={7284288,7285311,}
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int RN50_layer3__modules_5_conv1_weight[]={7285312,7547455,}
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int RN50_layer3__modules_5_bn1_running_mean[]={7547456,7547711,}
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int RN50_layer3__modules_5_bn1_running_var[]={7547712,7547967,}
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int RN50_layer3__modules_5_conv2_weight[]={7547968,8137791,}
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int RN50_layer3__modules_5_bn2_running_mean[]={8137792,8138047,}
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int RN50_layer3__modules_5_bn2_running_var[]={8138048,8138303,}
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int RN50_layer3__modules_5_conv3_weight[]={8138304,8400447,}
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int RN50_layer3__modules_5_bn3_running_mean[]={8400448,8401471,}
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int RN50_layer3__modules_5_bn3_running_var[]={8401472,8402495,}
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int RN50_layer4__modules_0_conv1_weight[]={8402496,8926783,}
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int RN50_layer4__modules_0_bn1_running_mean[]={8926784,8927295,}
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int RN50_layer4__modules_0_bn1_running_var[]={8927296,8927807,}
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int RN50_layer4__modules_0_conv2_weight[]={8927808,11287103,}
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int RN50_layer4__modules_0_bn2_running_mean[]={11287104,11287615,}
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int RN50_layer4__modules_0_bn2_running_var[]={11287616,11288127,}
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int RN50_layer4__modules_0_conv3_weight[]={11288128,12336703,}
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int RN50_layer4__modules_0_bn3_running_mean[]={12336704,12338751,}
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int RN50_layer4__modules_0_bn3_running_var[]={12338752,12340799,}
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int RN50_layer4__modules_0_downsample__modules_0_weight[]={12340800,14437951,}
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int RN50_layer4__modules_0_downsample__modules_1_running_mean[]={14437952,14439999,}
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int RN50_layer4__modules_0_downsample__modules_1_running_var[]={14440000,14442047,}
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int RN50_layer4__modules_1_conv1_weight[]={14442048,15490623,}
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int RN50_layer4__modules_1_bn1_running_mean[]={15490624,15491135,}
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int RN50_layer4__modules_1_bn1_running_var[]={15491136,15491647,}
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int RN50_layer4__modules_1_conv2_weight[]={15491648,17850943,}
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int RN50_layer4__modules_1_bn2_running_mean[]={17850944,17851455,}
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int RN50_layer4__modules_1_bn2_running_var[]={17851456,17851967,}
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int RN50_layer4__modules_1_conv3_weight[]={17851968,18900543,}
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int RN50_layer4__modules_1_bn3_running_mean[]={18900544,18902591,}
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int RN50_layer4__modules_1_bn3_running_var[]={18902592,18904639,}
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int RN50_layer4__modules_2_conv1_weight[]={18904640,19953215,}
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int RN50_layer4__modules_2_bn1_running_mean[]={19953216,19953727,}
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int RN50_layer4__modules_2_bn1_running_var[]={19953728,19954239,}
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int RN50_layer4__modules_2_conv2_weight[]={19954240,22313535,}
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int RN50_layer4__modules_2_bn2_running_mean[]={22313536,22314047,}
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int RN50_layer4__modules_2_bn2_running_var[]={22314048,22314559,}
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int RN50_layer4__modules_2_conv3_weight[]={22314560,23363135,}
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int RN50_layer4__modules_2_bn3_running_mean[]={23363136,23365183,}
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int RN50_layer4__modules_2_bn3_running_var[]={23365184,23367231,}
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int RN50_fc_weight[]={23367232,25415231,}
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int RN50_fc_bias[]={25415232,25416231,}
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@ -0,0 +1,486 @@
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from __future__ import print_function
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import os
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import sys
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torchvision
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from torchvision import datasets, transforms
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import torchvision.models as models
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import matplotlib.pyplot as plt
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import numpy as np
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import struct
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from struct import Struct
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CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
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resnet50 = models.resnet50(pretrained=True)
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torch.save(resnet50, CurrentPath+'params.pth')
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resnet50 = torch.load(CurrentPath+'params.pth')
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print("===========================")
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print("===========================")
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print("===========================")
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print(resnet50)
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print("===========================")
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print("===========================")
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print("===========================")
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# ss = resnet50.conv1.weight.cpu().detach().numpy().reshape(-1)
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# ss = ss.tolist()
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# strs = ''
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# # for s in ss:
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# # strs += str(s) + ","
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# bs = struct.pack("f",1.0)
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# f = open('data.hex', 'wb')
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# f.write(bs)
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# f.close()
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# print(strs)
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# ssa = ss.array()
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ResNet50 = {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"relu": "ReLU",
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"maxpool": "MaxPool2d",
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"layer1": {
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"_modules": {
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"0": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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"downsample": {
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"_modules": {
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"0": "Conv2d",
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"1": "BatchNorm2d",
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}
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}
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}
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}
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},
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"layer2": {
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"_modules": {
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"0": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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"downsample": {
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"_modules": {
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"0": "Conv2d",
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"1": "BatchNorm2d",
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}
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}
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},
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"1": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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},
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"2": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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},
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"3": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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}
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}
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},
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"layer3": {
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"_modules": {
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"0": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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"downsample": {
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"_modules": {
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"0": "Conv2d",
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"1": "BatchNorm2d",
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}
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}
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},
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"1": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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},
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"2": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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},
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"3": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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},
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"4": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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},
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"5": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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}
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}
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},
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"layer4": {
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"_modules": {
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"0": {
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"conv1": "Conv2d",
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"bn1": "BatchNorm2d",
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"conv2": "Conv2d",
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"bn2": "BatchNorm2d",
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"conv3": "Conv2d",
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"bn3": "BatchNorm2d",
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"relu": "ReLU",
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"downsample": {
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"_modules": {
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"0": "Conv2d",
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"1": "BatchNorm2d",
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}
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}
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},
|
||||
"1": {
|
||||
"conv1": "Conv2d",
|
||||
"bn1": "BatchNorm2d",
|
||||
"conv2": "Conv2d",
|
||||
"bn2": "BatchNorm2d",
|
||||
"conv3": "Conv2d",
|
||||
"bn3": "BatchNorm2d",
|
||||
"relu": "ReLU",
|
||||
},
|
||||
"2": {
|
||||
"conv1": "Conv2d",
|
||||
"bn1": "BatchNorm2d",
|
||||
"conv2": "Conv2d",
|
||||
"bn2": "BatchNorm2d",
|
||||
"conv3": "Conv2d",
|
||||
"bn3": "BatchNorm2d",
|
||||
"relu": "ReLU",
|
||||
}
|
||||
}
|
||||
},
|
||||
"avgpool": "AdaptiveAvgPool2d",
|
||||
"fc": "Linear"
|
||||
}
|
||||
|
||||
|
||||
weightfile = open(CurrentPath+'ResNet50Weight.cc', 'w')
|
||||
binaryfile = open(CurrentPath+'ResNet50Weight.bin', 'wb')
|
||||
currentbyte = 0
|
||||
|
||||
|
||||
def printDick(d, head, obj):
|
||||
global currentbyte
|
||||
global binaryfile
|
||||
strg = ""
|
||||
for item in d:
|
||||
if type(d[item]).__name__ == 'dict':
|
||||
objsub = getattr(obj, item, '')
|
||||
if objsub == '':
|
||||
objsub = obj[item]
|
||||
strg = strg + printDick(d[item], head+"_"+item, objsub)
|
||||
else:
|
||||
objsub = getattr(obj, item, '')
|
||||
if objsub == '':
|
||||
objsub = obj[item]
|
||||
if d[item] == "Conv2d":
|
||||
strg = strg + "int "+head+"_"+item+"_weight[]={"
|
||||
array = objsub.weight.cpu().detach().numpy().reshape(-1)
|
||||
|
||||
strg += str(currentbyte) + ","
|
||||
for a in array:
|
||||
bs = struct.pack("f", a)
|
||||
binaryfile.write(bs)
|
||||
currentbyte = currentbyte+1
|
||||
strg += str(currentbyte-1) + ","
|
||||
strg = strg + "}\n"
|
||||
if d[item] == "BatchNorm2d":
|
||||
strg = strg + "int "+head+"_"+item+"_running_mean[]={"
|
||||
array = objsub.running_mean.cpu().detach().numpy().reshape(-1)
|
||||
strg += str(currentbyte) + ","
|
||||
for a in array:
|
||||
bs = struct.pack("f", a)
|
||||
binaryfile.write(bs)
|
||||
currentbyte = currentbyte+1
|
||||
strg += str(currentbyte-1) + ","
|
||||
strg = strg + "}\n"
|
||||
|
||||
strg = strg + "int "+head+"_"+item+"_running_var[]={"
|
||||
array = objsub.running_var.cpu().detach().numpy().reshape(-1)
|
||||
strg += str(currentbyte) + ","
|
||||
for a in array:
|
||||
bs = struct.pack("f", a)
|
||||
binaryfile.write(bs)
|
||||
currentbyte = currentbyte+1
|
||||
strg += str(currentbyte-1) + ","
|
||||
strg = strg + "}\n"
|
||||
|
||||
if d[item] == "Linear":
|
||||
strg = strg + "int "+head+"_"+item+"_weight[]={"
|
||||
array = objsub.weight.cpu().detach().numpy().reshape(-1)
|
||||
strg += str(currentbyte) + ","
|
||||
for a in array:
|
||||
bs = struct.pack("f", a)
|
||||
binaryfile.write(bs)
|
||||
currentbyte = currentbyte+1
|
||||
strg += str(currentbyte-1) + ","
|
||||
strg = strg + "}\n"
|
||||
|
||||
strg = strg + "int "+head+"_"+item+"_bias[]={"
|
||||
array = objsub.bias.cpu().detach().numpy().reshape(-1)
|
||||
strg += str(currentbyte) + ","
|
||||
for a in array:
|
||||
bs = struct.pack("f", a)
|
||||
binaryfile.write(bs)
|
||||
currentbyte = currentbyte+1
|
||||
strg += str(currentbyte-1) + ","
|
||||
strg = strg + "}\n"
|
||||
|
||||
return strg
|
||||
|
||||
|
||||
ss = printDick(ResNet50, "RN50", resnet50)
|
||||
|
||||
weightfile.write(ss)
|
||||
|
||||
binaryfile.close()
|
||||
weightfile.close()
|
||||
|
||||
print(ss)
|
||||
|
||||
print("===========================")
|
||||
print("===========================")
|
||||
print("===========================")
|
||||
|
||||
|
||||
# ResNet(
|
||||
# (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
|
||||
# (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
|
||||
# (layer1): Sequential(
|
||||
# (0): Bottleneck(
|
||||
# (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# (downsample): Sequential(
|
||||
# (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# )
|
||||
# )
|
||||
# (1): Bottleneck(
|
||||
# (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# (2): Bottleneck(
|
||||
# (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# )
|
||||
# (layer2): Sequential(
|
||||
# (0): Bottleneck(
|
||||
# (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# (downsample): Sequential(
|
||||
# (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
||||
# (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# )
|
||||
# )
|
||||
# (1): Bottleneck(
|
||||
# (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# (2): Bottleneck(
|
||||
# (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# (3): Bottleneck(
|
||||
# (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# )
|
||||
# (layer3): Sequential(
|
||||
# (0): Bottleneck(
|
||||
# (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# (downsample): Sequential(
|
||||
# (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
||||
# (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# )
|
||||
# )
|
||||
# (1): Bottleneck(
|
||||
# (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# (2): Bottleneck(
|
||||
# (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# (3): Bottleneck(
|
||||
# (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# (4): Bottleneck(
|
||||
# (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# (5): Bottleneck(
|
||||
# (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# )
|
||||
# (layer4): Sequential(
|
||||
# (0): Bottleneck(
|
||||
# (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# (downsample): Sequential(
|
||||
# (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
||||
# (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# )
|
||||
# )
|
||||
# (1): Bottleneck(
|
||||
# (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# (2): Bottleneck(
|
||||
# (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||
# (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
||||
# (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
# (relu): ReLU(inplace=True)
|
||||
# )
|
||||
# )
|
||||
# (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
|
||||
# (fc): Linear(in_features=2048, out_features=1000, bias=True)
|
||||
# )
|
||||
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|
After Width: | Height: | Size: 2.3 MiB |
Loading…
Reference in New Issue