论文地址: CVPR2019 https://arxiv.org/abs/1902.09212
代码地址: deep-high-resolution-net.pytorch
作者:中科大 微软亚研 Ke Sun, Bin Xiao, Dong Liu, 百度Jingdong Wang
摘要
本文主要研究人体姿态估计问题，重点关注于可靠的高分辨率表示. 大多数现有的方法都是从由高到低产生的低分辨率表达中恢复高分辨率的表示，而我们提出的方法在整个过程中都保持高分辨率的表示.
讲论文的很多，但是怎么调试这个网络的比较少，尤其是刚开始看经典网络，竟不知道如何下手:
其实它的参数都是通过cfg的字典配置进去的
if __name__=="__main__":
cfg = {"MODEL":{
"INIT_WEIGHTS": None,
"NUM_JOINTS": 17,
"EXTRA":{
"FINAL_CONV_KERNEL": 1,
"PRETRAINED_LAYERS": None,
"STAGE2": {
"NUM_MODULES": 1,
"NUM_BRANCHES": 2,
"BLOCK": "BASIC",
"NUM_BLOCKS": [4,4],
"NUM_CHANNELS": [32,64],
"FUSE_METHOD": "SUM"
},
"STAGE3": {
"NUM_MODULES": 4,
"NUM_BRANCHES": 3,
"BLOCK": "BASIC",
"NUM_BLOCKS": [4,4,4],
"NUM_CHANNELS": [32,64,128],
"FUSE_METHOD": "SUM"
},
"STAGE4": {
"NUM_MODULES": 3,
"NUM_BRANCHES": 4,
"BLOCK": "BASIC",
"NUM_BLOCKS": [4,4,4,4],
"NUM_CHANNELS": [32,64,128,256],
"FUSE_METHOD": "SUM"
},
}
}
}
model = get_pose_net(cfg, True)
print(model)stem层算量
Model Summary
Name Input Size Output Size Parameters Multiply Adds (Flops)
----------------------------------------------------------------------------------------------------------------------------------
Conv2d_1 [1, 3, 256, 192] [1, 64, 128, 96] 1728 4.448 21233664 15.789
Conv2d_2 [1, 64, 128, 96] [1, 64, 64, 48] 36864 94.893 113246208 84.211
Total Parameters: 38,848
----------------------------------------------------------------------------------------------------------------------------------
Total Multiply Adds (For Convolution and Linear Layers only): 128.25 MFLOPs+layer1
Model Summary
Name Input Size Output Size Parameters Multiply Adds (Flops)
----------------------------------------------------------------------------------------------------------------------------------
Conv2d_1 [1, 3, 256, 192] [1, 64, 128, 96] 1728 0.532 21233664 2.118
Conv2d_2 [1, 64, 128, 96] [1, 64, 64, 48] 36864 11.341 113246208 11.294
Conv2d_3 [1, 64, 64, 48] [1, 64, 64, 48] 4096 1.260 12582912 1.255
Conv2d_4 [1, 64, 64, 48] [1, 64, 64, 48] 36864 11.341 113246208 11.294
Conv2d_5 [1, 64, 64, 48] [1, 256, 64, 48] 16384 5.040 50331648 5.020
Conv2d_6 [1, 64, 64, 48] [1, 256, 64, 48] 16384 5.040 50331648 5.020
Conv2d_7 [1, 256, 64, 48] [1, 64, 64, 48] 16384 5.040 50331648 5.020
Conv2d_8 [1, 64, 64, 48] [1, 64, 64, 48] 36864 11.341 113246208 11.294
Conv2d_9 [1, 64, 64, 48] [1, 256, 64, 48] 16384 5.040 50331648 5.020
Conv2d_10 [1, 256, 64, 48] [1, 64, 64, 48] 16384 5.040 50331648 5.020
Conv2d_11 [1, 64, 64, 48] [1, 64, 64, 48] 36864 11.341 113246208 11.294
Conv2d_12 [1, 64, 64, 48] [1, 256, 64, 48] 16384 5.040 50331648 5.020
Conv2d_13 [1, 256, 64, 48] [1, 64, 64, 48] 16384 5.040 50331648 5.020
Conv2d_14 [1, 64, 64, 48] [1, 64, 64, 48] 36864 11.341 113246208 11.294
Conv2d_15 [1, 64, 64, 48] [1, 256, 64, 48] 16384 5.040 50331648 5.020
Total Parameters: 325,056
----------------------------------------------------------------------------------------------------------------------------------
Total Multiply Adds (For Convolution and Linear Layers only): 956.25 MFLOPs+layer2:
Model Summary
Name Input Size Output Size Parameters Multiply Adds (Flops)
----------------------------------------------------------------------------------------------------------------------------------
Conv2d_1 [1, 3, 256, 192] [1, 64, 128, 96] 1728 0.184 21233664 1.172
Conv2d_2 [1, 64, 128, 96] [1, 64, 64, 48] 36864 3.933 113246208 6.253
Conv2d_3 [1, 64, 64, 48] [1, 64, 64, 48] 4096 0.437 12582912 0.695
Conv2d_4 [1, 64, 64, 48] [1, 64, 64, 48] 36864 3.933 113246208 6.253
Conv2d_5 [1, 64, 64, 48] [1, 256, 64, 48] 16384 1.748 50331648 2.779
Conv2d_6 [1, 64, 64, 48] [1, 256, 64, 48] 16384 1.748 50331648 2.779
Conv2d_7 [1, 256, 64, 48] [1, 64, 64, 48] 16384 1.748 50331648 2.779
Conv2d_8 [1, 64, 64, 48] [1, 64, 64, 48] 36864 3.933 113246208 6.253
Conv2d_9 [1, 64, 64, 48] [1, 256, 64, 48] 16384 1.748 50331648 2.779
Conv2d_10 [1, 256, 64, 48] [1, 64, 64, 48] 16384 1.748 50331648 2.779
Conv2d_11 [1, 64, 64, 48] [1, 64, 64, 48] 36864 3.933 113246208 6.253
Conv2d_12 [1, 64, 64, 48] [1, 256, 64, 48] 16384 1.748 50331648 2.779
Conv2d_13 [1, 256, 64, 48] [1, 64, 64, 48] 16384 1.748 50331648 2.779
Conv2d_14 [1, 64, 64, 48] [1, 64, 64, 48] 36864 3.933 113246208 6.253
Conv2d_15 [1, 64, 64, 48] [1, 256, 64, 48] 16384 1.748 50331648 2.779
Conv2d_16 [1, 256, 64, 48] [1, 32, 64, 48] 73728 7.866 226492416 12.505
Conv2d_17 [1, 256, 64, 48] [1, 64, 32, 24] 147456 15.732 113246208 6.253
Conv2d_18 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.983 28311552 1.563
Conv2d_19 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.983 28311552 1.563
Conv2d_20 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.983 28311552 1.563
Conv2d_21 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.983 28311552 1.563
Conv2d_22 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.983 28311552 1.563
Conv2d_23 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.983 28311552 1.563
Conv2d_24 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.983 28311552 1.563
Conv2d_25 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.983 28311552 1.563
Conv2d_26 [1, 64, 32, 24] [1, 64, 32, 24] 36864 3.933 28311552 1.563
Conv2d_27 [1, 64, 32, 24] [1, 64, 32, 24] 36864 3.933 28311552 1.563
Conv2d_28 [1, 64, 32, 24] [1, 64, 32, 24] 36864 3.933 28311552 1.563
Conv2d_29 [1, 64, 32, 24] [1, 64, 32, 24] 36864 3.933 28311552 1.563
Conv2d_30 [1, 64, 32, 24] [1, 64, 32, 24] 36864 3.933 28311552 1.563
Conv2d_31 [1, 64, 32, 24] [1, 64, 32, 24] 36864 3.933 28311552 1.563
Conv2d_32 [1, 64, 32, 24] [1, 64, 32, 24] 36864 3.933 28311552 1.563
Conv2d_33 [1, 64, 32, 24] [1, 64, 32, 24] 36864 3.933 28311552 1.563
Conv2d_34 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.219 1572864 0.087
Conv2d_35 [1, 32, 64, 48] [1, 64, 32, 24] 18432 1.967 14155776 0.782
Total Parameters: 937,280
----------------------------------------------------------------------------------------------------------------------------------
Total Multiply Adds (For Convolution and Linear Layers only): 1,727.25 MFLOPs+layer3:
Model Summary
Name Input Size Output Size Parameters Multiply Adds (Flops)
----------------------------------------------------------------------------------------------------------------------------------
Conv2d_1 [1, 3, 256, 192] [1, 64, 128, 96] 1728 0.022 21233664 0.449
Conv2d_2 [1, 64, 128, 96] [1, 64, 64, 48] 36864 0.471 113246208 2.395
Conv2d_3 [1, 64, 64, 48] [1, 64, 64, 48] 4096 0.052 12582912 0.266
Conv2d_4 [1, 64, 64, 48] [1, 64, 64, 48] 36864 0.471 113246208 2.395
Conv2d_5 [1, 64, 64, 48] [1, 256, 64, 48] 16384 0.209 50331648 1.064
Conv2d_6 [1, 64, 64, 48] [1, 256, 64, 48] 16384 0.209 50331648 1.064
Conv2d_7 [1, 256, 64, 48] [1, 64, 64, 48] 16384 0.209 50331648 1.064
Conv2d_8 [1, 64, 64, 48] [1, 64, 64, 48] 36864 0.471 113246208 2.395
Conv2d_9 [1, 64, 64, 48] [1, 256, 64, 48] 16384 0.209 50331648 1.064
Conv2d_10 [1, 256, 64, 48] [1, 64, 64, 48] 16384 0.209 50331648 1.064
Conv2d_11 [1, 64, 64, 48] [1, 64, 64, 48] 36864 0.471 113246208 2.395
Conv2d_12 [1, 64, 64, 48] [1, 256, 64, 48] 16384 0.209 50331648 1.064
Conv2d_13 [1, 256, 64, 48] [1, 64, 64, 48] 16384 0.209 50331648 1.064
Conv2d_14 [1, 64, 64, 48] [1, 64, 64, 48] 36864 0.471 113246208 2.395
Conv2d_15 [1, 64, 64, 48] [1, 256, 64, 48] 16384 0.209 50331648 1.064
Conv2d_16 [1, 256, 64, 48] [1, 32, 64, 48] 73728 0.941 226492416 4.790
Conv2d_17 [1, 256, 64, 48] [1, 64, 32, 24] 147456 1.883 113246208 2.395
Conv2d_18 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_19 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_20 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_21 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_22 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_23 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_24 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_25 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_26 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_27 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_28 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_29 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_30 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_31 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_32 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_33 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_34 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.026 1572864 0.033
Conv2d_35 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.235 14155776 0.299
Conv2d_36 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.941 14155776 0.299
Conv2d_37 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_38 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_39 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_40 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_41 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_42 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_43 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_44 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_45 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_46 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_47 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_48 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_49 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_50 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_51 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_52 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_53 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_54 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_55 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_56 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_57 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_58 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_59 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_60 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_61 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.026 1572864 0.033
Conv2d_62 [1, 128, 16, 12] [1, 32, 16, 12] 4096 0.052 786432 0.017
Conv2d_63 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.235 14155776 0.299
Conv2d_64 [1, 128, 16, 12] [1, 64, 16, 12] 8192 0.105 1572864 0.033
Conv2d_65 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.118 7077888 0.150
Conv2d_66 [1, 32, 32, 24] [1, 128, 16, 12] 36864 0.471 7077888 0.150
Conv2d_67 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.941 14155776 0.299
Conv2d_68 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_69 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_70 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_71 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_72 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_73 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_74 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_75 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_76 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_77 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_78 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_79 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_80 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_81 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_82 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_83 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_84 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_85 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_86 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_87 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_88 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_89 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_90 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_91 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_92 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.026 1572864 0.033
Conv2d_93 [1, 128, 16, 12] [1, 32, 16, 12] 4096 0.052 786432 0.017
Conv2d_94 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.235 14155776 0.299
Conv2d_95 [1, 128, 16, 12] [1, 64, 16, 12] 8192 0.105 1572864 0.033
Conv2d_96 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.118 7077888 0.150
Conv2d_97 [1, 32, 32, 24] [1, 128, 16, 12] 36864 0.471 7077888 0.150
Conv2d_98 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.941 14155776 0.299
Conv2d_99 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_100 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_101 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_102 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_103 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_104 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_105 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_106 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_107 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_108 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_109 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_110 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_111 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_112 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_113 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_114 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_115 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_116 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_117 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_118 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_119 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_120 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_121 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_122 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_123 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.026 1572864 0.033
Conv2d_124 [1, 128, 16, 12] [1, 32, 16, 12] 4096 0.052 786432 0.017
Conv2d_125 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.235 14155776 0.299
Conv2d_126 [1, 128, 16, 12] [1, 64, 16, 12] 8192 0.105 1572864 0.033
Conv2d_127 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.118 7077888 0.150
Conv2d_128 [1, 32, 32, 24] [1, 128, 16, 12] 36864 0.471 7077888 0.150
Conv2d_129 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.941 14155776 0.299
Conv2d_130 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_131 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_132 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_133 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_134 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_135 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_136 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_137 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.118 28311552 0.599
Conv2d_138 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_139 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_140 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_141 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_142 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_143 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_144 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_145 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.471 28311552 0.599
Conv2d_146 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_147 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_148 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_149 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_150 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_151 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_152 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_153 [1, 128, 16, 12] [1, 128, 16, 12] 147456 1.883 28311552 0.599
Conv2d_154 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.026 1572864 0.033
Conv2d_155 [1, 128, 16, 12] [1, 32, 16, 12] 4096 0.052 786432 0.017
Conv2d_156 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.235 14155776 0.299
Conv2d_157 [1, 128, 16, 12] [1, 64, 16, 12] 8192 0.105 1572864 0.033
Conv2d_158 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.118 7077888 0.150
Conv2d_159 [1, 32, 32, 24] [1, 128, 16, 12] 36864 0.471 7077888 0.150
Conv2d_160 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.941 14155776 0.299
Total Parameters: 7,832,896
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Total Multiply Adds (For Convolution and Linear Layers only): 4,509.75 MFLOPs+layer4:
Model Summary
Name Input Size Output Size Parameters Multiply Adds (Flops)
----------------------------------------------------------------------------------------------------------------------------------
Conv2d_1 [1, 3, 256, 192] [1, 64, 128, 96] 1728 0.006 21233664 0.278
Conv2d_2 [1, 64, 128, 96] [1, 64, 64, 48] 36864 0.129 113246208 1.481
Conv2d_3 [1, 64, 64, 48] [1, 64, 64, 48] 4096 0.014 12582912 0.165
Conv2d_4 [1, 64, 64, 48] [1, 64, 64, 48] 36864 0.129 113246208 1.481
Conv2d_5 [1, 64, 64, 48] [1, 256, 64, 48] 16384 0.057 50331648 0.658
Conv2d_6 [1, 64, 64, 48] [1, 256, 64, 48] 16384 0.057 50331648 0.658
Conv2d_7 [1, 256, 64, 48] [1, 64, 64, 48] 16384 0.057 50331648 0.658
Conv2d_8 [1, 64, 64, 48] [1, 64, 64, 48] 36864 0.129 113246208 1.481
Conv2d_9 [1, 64, 64, 48] [1, 256, 64, 48] 16384 0.057 50331648 0.658
Conv2d_10 [1, 256, 64, 48] [1, 64, 64, 48] 16384 0.057 50331648 0.658
Conv2d_11 [1, 64, 64, 48] [1, 64, 64, 48] 36864 0.129 113246208 1.481
Conv2d_12 [1, 64, 64, 48] [1, 256, 64, 48] 16384 0.057 50331648 0.658
Conv2d_13 [1, 256, 64, 48] [1, 64, 64, 48] 16384 0.057 50331648 0.658
Conv2d_14 [1, 64, 64, 48] [1, 64, 64, 48] 36864 0.129 113246208 1.481
Conv2d_15 [1, 64, 64, 48] [1, 256, 64, 48] 16384 0.057 50331648 0.658
Conv2d_16 [1, 256, 64, 48] [1, 32, 64, 48] 73728 0.258 226492416 2.963
Conv2d_17 [1, 256, 64, 48] [1, 64, 32, 24] 147456 0.517 113246208 1.481
Conv2d_18 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_19 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_20 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_21 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_22 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_23 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_24 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_25 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_26 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_27 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_28 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_29 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_30 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_31 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_32 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_33 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_34 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.007 1572864 0.021
Conv2d_35 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.065 14155776 0.185
Conv2d_36 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.258 14155776 0.185
Conv2d_37 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_38 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_39 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_40 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_41 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_42 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_43 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_44 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_45 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_46 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_47 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_48 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_49 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_50 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_51 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_52 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_53 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_54 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_55 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_56 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_57 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_58 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_59 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_60 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_61 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.007 1572864 0.021
Conv2d_62 [1, 128, 16, 12] [1, 32, 16, 12] 4096 0.014 786432 0.010
Conv2d_63 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.065 14155776 0.185
Conv2d_64 [1, 128, 16, 12] [1, 64, 16, 12] 8192 0.029 1572864 0.021
Conv2d_65 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.032 7077888 0.093
Conv2d_66 [1, 32, 32, 24] [1, 128, 16, 12] 36864 0.129 7077888 0.093
Conv2d_67 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.258 14155776 0.185
Conv2d_68 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_69 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_70 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_71 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_72 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_73 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_74 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_75 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_76 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_77 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_78 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_79 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_80 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_81 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_82 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_83 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_84 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_85 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_86 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_87 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_88 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_89 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_90 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_91 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_92 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.007 1572864 0.021
Conv2d_93 [1, 128, 16, 12] [1, 32, 16, 12] 4096 0.014 786432 0.010
Conv2d_94 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.065 14155776 0.185
Conv2d_95 [1, 128, 16, 12] [1, 64, 16, 12] 8192 0.029 1572864 0.021
Conv2d_96 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.032 7077888 0.093
Conv2d_97 [1, 32, 32, 24] [1, 128, 16, 12] 36864 0.129 7077888 0.093
Conv2d_98 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.258 14155776 0.185
Conv2d_99 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_100 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_101 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_102 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_103 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_104 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_105 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_106 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_107 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_108 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_109 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_110 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_111 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_112 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_113 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_114 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_115 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_116 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_117 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_118 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_119 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_120 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_121 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_122 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_123 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.007 1572864 0.021
Conv2d_124 [1, 128, 16, 12] [1, 32, 16, 12] 4096 0.014 786432 0.010
Conv2d_125 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.065 14155776 0.185
Conv2d_126 [1, 128, 16, 12] [1, 64, 16, 12] 8192 0.029 1572864 0.021
Conv2d_127 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.032 7077888 0.093
Conv2d_128 [1, 32, 32, 24] [1, 128, 16, 12] 36864 0.129 7077888 0.093
Conv2d_129 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.258 14155776 0.185
Conv2d_130 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_131 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_132 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_133 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_134 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_135 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_136 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_137 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_138 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_139 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_140 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_141 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_142 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_143 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_144 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_145 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_146 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_147 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_148 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_149 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_150 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_151 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_152 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_153 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_154 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.007 1572864 0.021
Conv2d_155 [1, 128, 16, 12] [1, 32, 16, 12] 4096 0.014 786432 0.010
Conv2d_156 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.065 14155776 0.185
Conv2d_157 [1, 128, 16, 12] [1, 64, 16, 12] 8192 0.029 1572864 0.021
Conv2d_158 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.032 7077888 0.093
Conv2d_159 [1, 32, 32, 24] [1, 128, 16, 12] 36864 0.129 7077888 0.093
Conv2d_160 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.258 14155776 0.185
Conv2d_161 [1, 128, 16, 12] [1, 256, 8, 6] 294912 1.033 14155776 0.185
Conv2d_162 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_163 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_164 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_165 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_166 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_167 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_168 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_169 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_170 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_171 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_172 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_173 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_174 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_175 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_176 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_177 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_178 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_179 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_180 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_181 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_182 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_183 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_184 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_185 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_186 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_187 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_188 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_189 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_190 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_191 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_192 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_193 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_194 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.007 1572864 0.021
Conv2d_195 [1, 128, 16, 12] [1, 32, 16, 12] 4096 0.014 786432 0.010
Conv2d_196 [1, 256, 8, 6] [1, 32, 8, 6] 8192 0.029 393216 0.005
Conv2d_197 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.065 14155776 0.185
Conv2d_198 [1, 128, 16, 12] [1, 64, 16, 12] 8192 0.029 1572864 0.021
Conv2d_199 [1, 256, 8, 6] [1, 64, 8, 6] 16384 0.057 786432 0.010
Conv2d_200 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.032 7077888 0.093
Conv2d_201 [1, 32, 32, 24] [1, 128, 16, 12] 36864 0.129 7077888 0.093
Conv2d_202 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.258 14155776 0.185
Conv2d_203 [1, 256, 8, 6] [1, 128, 8, 6] 32768 0.115 1572864 0.021
Conv2d_204 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.032 7077888 0.093
Conv2d_205 [1, 32, 32, 24] [1, 32, 16, 12] 9216 0.032 1769472 0.023
Conv2d_206 [1, 32, 16, 12] [1, 256, 8, 6] 73728 0.258 3538944 0.046
Conv2d_207 [1, 64, 32, 24] [1, 64, 16, 12] 36864 0.129 7077888 0.093
Conv2d_208 [1, 64, 16, 12] [1, 256, 8, 6] 147456 0.517 7077888 0.093
Conv2d_209 [1, 128, 16, 12] [1, 256, 8, 6] 294912 1.033 14155776 0.185
Conv2d_210 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_211 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_212 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_213 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_214 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_215 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_216 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_217 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_218 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_219 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_220 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_221 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_222 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_223 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_224 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_225 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_226 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_227 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_228 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_229 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_230 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_231 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_232 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_233 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_234 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_235 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_236 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_237 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_238 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_239 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_240 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_241 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_242 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.007 1572864 0.021
Conv2d_243 [1, 128, 16, 12] [1, 32, 16, 12] 4096 0.014 786432 0.010
Conv2d_244 [1, 256, 8, 6] [1, 32, 8, 6] 8192 0.029 393216 0.005
Conv2d_245 [1, 32, 64, 48] [1, 64, 32, 24] 18432 0.065 14155776 0.185
Conv2d_246 [1, 128, 16, 12] [1, 64, 16, 12] 8192 0.029 1572864 0.021
Conv2d_247 [1, 256, 8, 6] [1, 64, 8, 6] 16384 0.057 786432 0.010
Conv2d_248 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.032 7077888 0.093
Conv2d_249 [1, 32, 32, 24] [1, 128, 16, 12] 36864 0.129 7077888 0.093
Conv2d_250 [1, 64, 32, 24] [1, 128, 16, 12] 73728 0.258 14155776 0.185
Conv2d_251 [1, 256, 8, 6] [1, 128, 8, 6] 32768 0.115 1572864 0.021
Conv2d_252 [1, 32, 64, 48] [1, 32, 32, 24] 9216 0.032 7077888 0.093
Conv2d_253 [1, 32, 32, 24] [1, 32, 16, 12] 9216 0.032 1769472 0.023
Conv2d_254 [1, 32, 16, 12] [1, 256, 8, 6] 73728 0.258 3538944 0.046
Conv2d_255 [1, 64, 32, 24] [1, 64, 16, 12] 36864 0.129 7077888 0.093
Conv2d_256 [1, 64, 16, 12] [1, 256, 8, 6] 147456 0.517 7077888 0.093
Conv2d_257 [1, 128, 16, 12] [1, 256, 8, 6] 294912 1.033 14155776 0.185
Conv2d_258 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_259 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_260 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_261 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_262 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_263 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_264 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_265 [1, 32, 64, 48] [1, 32, 64, 48] 9216 0.032 28311552 0.370
Conv2d_266 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_267 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_268 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_269 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_270 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_271 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_272 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_273 [1, 64, 32, 24] [1, 64, 32, 24] 36864 0.129 28311552 0.370
Conv2d_274 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_275 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_276 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_277 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_278 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_279 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_280 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_281 [1, 128, 16, 12] [1, 128, 16, 12] 147456 0.517 28311552 0.370
Conv2d_282 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_283 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_284 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_285 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_286 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_287 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_288 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_289 [1, 256, 8, 6] [1, 256, 8, 6] 589824 2.067 28311552 0.370
Conv2d_290 [1, 64, 32, 24] [1, 32, 32, 24] 2048 0.007 1572864 0.021
Conv2d_291 [1, 128, 16, 12] [1, 32, 16, 12] 4096 0.014 786432 0.010
Conv2d_292 [1, 256, 8, 6] [1, 32, 8, 6] 8192 0.029 393216 0.005
Conv2d_293 [1, 32, 64, 48] [1, 17, 64, 48] 561 0.002 1671168 0.022
Total Parameters: 28,536,113
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Total Multiply Adds (For Convolution and Linear Layers only): 7,290.84375 MFLOPs