2018-05-07 23:30:29.213594: I tensorflow/tools/benchmark/benchmark_model.cc:465] Graph: [testdata/mobilenet_v1_1.0_224_quant_frozen.pb] 2018-05-07 23:30:29.213651: I tensorflow/tools/benchmark/benchmark_model.cc:466] Init ops: 2018-05-07 23:30:29.213662: I tensorflow/tools/benchmark/benchmark_model.cc:467] Input layers: [input] 2018-05-07 23:30:29.213672: I tensorflow/tools/benchmark/benchmark_model.cc:468] Input shapes: [1,224,224,3] 2018-05-07 23:30:29.213678: I tensorflow/tools/benchmark/benchmark_model.cc:469] Input types: [float] 2018-05-07 23:30:29.213684: I tensorflow/tools/benchmark/benchmark_model.cc:470] Output layers: [MobilenetV1/Predictions/Reshape_1] 2018-05-07 23:30:29.213692: I tensorflow/tools/benchmark/benchmark_model.cc:471] Target layers: [] 2018-05-07 23:30:29.213707: I tensorflow/tools/benchmark/benchmark_model.cc:472] Num runs: [1000] 2018-05-07 23:30:29.213720: I tensorflow/tools/benchmark/benchmark_model.cc:473] Inter-inference delay (seconds): [-1.0] 2018-05-07 23:30:29.213732: I tensorflow/tools/benchmark/benchmark_model.cc:474] Inter-benchmark delay (seconds): [-1.0] 2018-05-07 23:30:29.213745: I tensorflow/tools/benchmark/benchmark_model.cc:476] Num threads: [1] 2018-05-07 23:30:29.213757: I tensorflow/tools/benchmark/benchmark_model.cc:477] Benchmark name: [] 2018-05-07 23:30:29.213770: I tensorflow/tools/benchmark/benchmark_model.cc:478] Output prefix: [] 2018-05-07 23:30:29.213785: I tensorflow/tools/benchmark/benchmark_model.cc:479] Show sizes: [0] 2018-05-07 23:30:29.213851: I tensorflow/tools/benchmark/benchmark_model.cc:480] Warmup runs: [1] 2018-05-07 23:30:29.213864: I tensorflow/tools/benchmark/benchmark_model.cc:251] Loading TensorFlow. 2018-05-07 23:30:29.213878: I tensorflow/tools/benchmark/benchmark_model.cc:258] Got config, 0 devices 2018-05-07 23:30:29.246226: I tensorflow/tools/benchmark/benchmark_model.cc:492] Initialized session in 0.032349s 2018-05-07 23:30:29.246278: I tensorflow/tools/benchmark/benchmark_model.cc:323] Running benchmark for max 1 iterations, max -1 seconds without detailed stat logging, with -1s sleep between inferences 2018-05-07 23:30:29.585203: I tensorflow/tools/benchmark/benchmark_model.cc:357] count=1 curr=338633 2018-05-07 23:30:29.585265: I tensorflow/tools/benchmark/benchmark_model.cc:323] Running benchmark for max 1000 iterations, max 10 seconds without detailed stat logging, with -1s sleep between inferences 2018-05-07 23:30:39.615237: I tensorflow/tools/benchmark/benchmark_model.cc:357] count=263 first=38757 curr=37212 min=36471 max=64485 avg=38078.9 std=3061 2018-05-07 23:30:39.615294: I tensorflow/tools/benchmark/benchmark_model.cc:323] Running benchmark for max 1000 iterations, max 10 seconds with detailed stat logging, with -1s sleep between inferences 2018-05-07 23:30:49.736718: I tensorflow/tools/benchmark/benchmark_model.cc:357] count=246 first=39609 curr=39940 min=37563 max=72329 avg=40802.2 std=5133 2018-05-07 23:30:49.736768: I tensorflow/tools/benchmark/benchmark_model.cc:596] Average inference timings in us: Warmup: 338633, no stats: 38078, with stats: 40802 2018-05-07 23:30:49.738262: I tensorflow/core/util/stat_summarizer.cc:358] Number of nodes executed: 179 2018-05-07 23:30:49.738431: I tensorflow/core/util/stat_summarizer.cc:468] ============================== Run Order ============================== 2018-05-07 23:30:49.738450: I tensorflow/core/util/stat_summarizer.cc:468] [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] 2018-05-07 23:30:49.738459: I tensorflow/core/util/stat_summarizer.cc:468] NoOp 0.000 0.009 0.116 0.288% 0.288% 0.000 1 _SOURCE 2018-05-07 23:30:49.738471: I tensorflow/core/util/stat_summarizer.cc:468] _Arg 0.122 0.006 0.004 0.009% 0.297% 0.000 1 _arg_input_0_0 2018-05-07 23:30:49.738483: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.127 0.005 0.003 0.008% 0.305% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_0/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738494: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.146 0.003 0.003 0.007% 0.312% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm_Fold/bias 2018-05-07 23:30:49.738507: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.150 0.004 0.003 0.009% 0.321% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_0/act_quant/min 2018-05-07 23:30:49.738519: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.154 0.004 0.003 0.007% 0.327% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_0/act_quant/max 2018-05-07 23:30:49.738531: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.158 0.003 0.003 0.007% 0.334% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738542: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.162 0.003 0.003 0.006% 0.340% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738553: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.165 0.004 0.003 0.007% 0.347% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738565: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.169 0.003 0.003 0.006% 0.354% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_1_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738576: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.173 0.003 0.002 0.006% 0.360% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738586: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.176 0.003 0.002 0.006% 0.365% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738598: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.179 0.003 0.003 0.007% 0.372% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_2_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738609: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.183 0.003 0.002 0.006% 0.379% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738623: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.187 0.003 0.003 0.007% 0.386% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_2_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738635: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.191 0.003 0.002 0.006% 0.392% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738645: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.194 0.003 0.003 0.007% 0.398% 0.000 1 MobilenetV1/Logits/Conv2d_1c_1x1/biases 2018-05-07 23:30:49.738655: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.198 0.003 0.003 0.007% 0.405% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738666: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.202 0.003 0.002 0.006% 0.411% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738678: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.205 0.003 0.003 0.007% 0.418% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_12_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738689: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.209 0.003 0.003 0.006% 0.424% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_3_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738699: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.212 0.003 0.002 0.006% 0.430% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738708: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.216 0.002 0.002 0.006% 0.436% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738718: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.219 0.002 0.003 0.006% 0.442% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_4_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738728: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.223 0.002 0.002 0.006% 0.448% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738740: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.226 0.003 0.003 0.007% 0.455% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_4_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738751: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.230 0.001 0.002 0.006% 0.461% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738787: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.233 0.002 0.002 0.005% 0.466% 0.000 1 MobilenetV1/Logits/Conv2d_1c_1x1/act_quant/min 2018-05-07 23:30:49.738800: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.236 0.002 0.003 0.007% 0.473% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_5_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738814: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.240 0.002 0.002 0.006% 0.479% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738826: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.244 0.002 0.003 0.007% 0.486% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_12_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738837: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.247 0.002 0.003 0.006% 0.493% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_5_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738848: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.251 0.002 0.002 0.006% 0.498% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738859: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.255 0.001 0.002 0.006% 0.505% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738870: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.258 0.002 0.003 0.006% 0.511% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_6_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738881: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.262 0.002 0.002 0.006% 0.517% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738893: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.265 0.002 0.002 0.005% 0.521% 0.000 1 MobilenetV1/Logits/Conv2d_1c_1x1/act_quant/max 2018-05-07 23:30:49.738904: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.268 0.002 0.003 0.006% 0.528% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_6_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738914: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.271 0.001 0.002 0.005% 0.533% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738924: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.274 0.002 0.003 0.007% 0.540% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_7_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738934: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.278 0.002 0.002 0.005% 0.545% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738946: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.281 0.002 0.003 0.006% 0.551% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_13_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738957: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.284 0.004 0.002 0.006% 0.557% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_7_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.738969: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.287 0.002 0.002 0.006% 0.563% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738983: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.291 0.002 0.002 0.006% 0.569% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.738994: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.294 0.002 0.003 0.006% 0.575% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_8_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739005: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.297 0.001 0.002 0.005% 0.580% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.739017: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.300 0.002 0.003 0.006% 0.587% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_8_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739029: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.304 0.002 0.002 0.005% 0.592% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.739040: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.307 0.002 0.003 0.006% 0.598% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_9_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739056: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.310 0.002 0.002 0.005% 0.604% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.739067: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.313 0.002 0.003 0.006% 0.610% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_13_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739079: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.316 0.002 0.002 0.006% 0.616% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_9_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739088: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.320 0.002 0.002 0.005% 0.622% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.739098: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.323 0.002 0.002 0.006% 0.627% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.739108: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.326 0.002 0.003 0.006% 0.634% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739119: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.330 0.001 0.002 0.006% 0.639% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.739131: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.333 0.002 0.003 0.006% 0.645% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739142: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.336 0.002 0.002 0.006% 0.651% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.739156: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.339 0.001 0.003 0.006% 0.657% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739167: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.343 0.002 0.002 0.006% 0.663% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm_Fold/bias 2018-05-07 23:30:49.739179: I tensorflow/core/util/stat_summarizer.cc:468] Const 0.346 0.002 0.003 0.006% 0.669% 0.000 1 MobilenetV1/Logits/Conv2d_1c_1x1/weights_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739191: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 0.153 2.982 3.152 7.797% 8.466% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_0/Conv2D_Fold 2018-05-07 23:30:49.739202: I tensorflow/core/util/stat_summarizer.cc:468] Add 3.314 0.488 0.495 1.223% 9.690% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_0/add_fold 2018-05-07 23:30:49.739213: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 3.811 0.069 0.078 0.193% 9.883% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_0/Relu6 2018-05-07 23:30:49.739237: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 3.892 0.191 0.314 0.776% 10.659% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_0/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739249: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 4.211 2.591 2.780 6.879% 17.538% 1606.784 1 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise_Fold 2018-05-07 23:30:49.739261: I tensorflow/core/util/stat_summarizer.cc:468] Add 6.997 0.474 0.491 1.216% 18.754% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/add_fold 2018-05-07 23:30:49.739275: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 7.491 0.072 0.077 0.192% 18.945% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6 2018-05-07 23:30:49.739287: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 7.570 0.175 0.221 0.546% 19.491% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739298: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 7.795 0.915 1.216 3.008% 22.499% 3211.264 1 MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Conv2D_Fold 2018-05-07 23:30:49.739309: I tensorflow/core/util/stat_summarizer.cc:468] Add 9.018 0.947 0.986 2.441% 24.940% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_1_pointwise/add_fold 2018-05-07 23:30:49.739320: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 10.008 0.173 0.190 0.470% 25.409% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6 2018-05-07 23:30:49.739331: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 10.200 0.895 0.729 1.803% 27.213% 3211.264 1 MobilenetV1/MobilenetV1/Conv2d_1_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739357: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 10.936 1.251 1.214 3.004% 30.217% 805.120 1 MobilenetV1/MobilenetV1/Conv2d_2_depthwise/depthwise_Fold 2018-05-07 23:30:49.739370: I tensorflow/core/util/stat_summarizer.cc:468] Add 12.157 0.242 0.251 0.621% 30.838% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_2_depthwise/add_fold 2018-05-07 23:30:49.739381: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 12.410 0.041 0.039 0.097% 30.935% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6 2018-05-07 23:30:49.739392: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 12.451 0.098 0.097 0.241% 31.176% 802.816 1 MobilenetV1/MobilenetV1/Conv2d_2_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739403: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 12.552 0.811 0.806 1.995% 33.170% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Conv2D_Fold 2018-05-07 23:30:49.739414: I tensorflow/core/util/stat_summarizer.cc:468] Add 13.362 0.473 0.491 1.215% 34.385% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_2_pointwise/add_fold 2018-05-07 23:30:49.739426: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 13.855 0.082 0.077 0.190% 34.575% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6 2018-05-07 23:30:49.739436: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 13.933 0.183 0.198 0.490% 35.065% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_2_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739447: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 14.136 2.029 2.060 5.097% 40.162% 1610.240 1 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/depthwise_Fold 2018-05-07 23:30:49.739458: I tensorflow/core/util/stat_summarizer.cc:468] Add 16.202 0.474 0.494 1.222% 41.383% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/add_fold 2018-05-07 23:30:49.739473: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 16.697 0.077 0.079 0.196% 41.580% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6 2018-05-07 23:30:49.739484: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 16.778 0.179 0.192 0.474% 42.054% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739494: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 16.975 1.379 1.484 3.672% 45.726% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Conv2D_Fold 2018-05-07 23:30:49.739504: I tensorflow/core/util/stat_summarizer.cc:468] Add 18.464 0.479 0.490 1.213% 46.939% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_3_pointwise/add_fold 2018-05-07 23:30:49.739513: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 18.957 0.072 0.076 0.189% 47.128% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6 2018-05-07 23:30:49.739524: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 19.034 0.179 0.193 0.479% 47.606% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_3_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739538: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 19.233 0.506 0.539 1.334% 48.940% 406.016 1 MobilenetV1/MobilenetV1/Conv2d_4_depthwise/depthwise_Fold 2018-05-07 23:30:49.739549: I tensorflow/core/util/stat_summarizer.cc:468] Add 19.776 0.121 0.126 0.313% 49.252% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_4_depthwise/add_fold 2018-05-07 23:30:49.739580: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 19.904 0.019 0.019 0.048% 49.300% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6 2018-05-07 23:30:49.739591: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 19.924 0.050 0.050 0.124% 49.424% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_4_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739603: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 19.978 0.666 0.713 1.764% 51.188% 802.816 1 MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Conv2D_Fold 2018-05-07 23:30:49.739639: I tensorflow/core/util/stat_summarizer.cc:468] Add 20.694 0.236 0.247 0.611% 51.799% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_4_pointwise/add_fold 2018-05-07 23:30:49.739654: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 20.943 0.036 0.037 0.092% 51.891% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6 2018-05-07 23:30:49.739665: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 20.982 0.089 0.095 0.234% 52.125% 802.816 1 MobilenetV1/MobilenetV1/Conv2d_4_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739677: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 21.079 0.928 0.982 2.430% 54.556% 812.032 1 MobilenetV1/MobilenetV1/Conv2d_5_depthwise/depthwise_Fold 2018-05-07 23:30:49.739688: I tensorflow/core/util/stat_summarizer.cc:468] Add 22.066 0.235 0.244 0.605% 55.161% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_5_depthwise/add_fold 2018-05-07 23:30:49.739703: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 22.312 0.036 0.036 0.090% 55.251% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6 2018-05-07 23:30:49.739715: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 22.349 0.085 0.092 0.228% 55.479% 802.816 1 MobilenetV1/MobilenetV1/Conv2d_5_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739725: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 22.445 1.245 1.259 3.115% 58.594% 802.816 1 MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Conv2D_Fold 2018-05-07 23:30:49.739736: I tensorflow/core/util/stat_summarizer.cc:468] Add 23.708 0.239 0.246 0.609% 59.203% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_5_pointwise/add_fold 2018-05-07 23:30:49.739751: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 23.956 0.035 0.036 0.090% 59.293% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6 2018-05-07 23:30:49.739765: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 23.994 0.090 0.093 0.230% 59.523% 802.816 1 MobilenetV1/MobilenetV1/Conv2d_5_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739776: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 24.090 0.267 0.253 0.627% 60.149% 209.920 1 MobilenetV1/MobilenetV1/Conv2d_6_depthwise/depthwise_Fold 2018-05-07 23:30:49.739787: I tensorflow/core/util/stat_summarizer.cc:468] Add 24.346 0.064 0.064 0.158% 60.308% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_6_depthwise/add_fold 2018-05-07 23:30:49.739798: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 24.412 0.009 0.009 0.023% 60.331% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6 2018-05-07 23:30:49.739810: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 24.422 0.027 0.026 0.064% 60.394% 200.704 1 MobilenetV1/MobilenetV1/Conv2d_6_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739821: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 24.450 0.833 0.710 1.755% 62.150% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Conv2D_Fold 2018-05-07 23:30:49.739835: I tensorflow/core/util/stat_summarizer.cc:468] Add 25.163 0.121 0.125 0.309% 62.459% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_6_pointwise/add_fold 2018-05-07 23:30:49.739846: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 25.290 0.019 0.019 0.046% 62.505% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6 2018-05-07 23:30:49.739857: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 25.310 0.048 0.048 0.119% 62.624% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_6_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739868: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 25.361 0.461 0.487 1.205% 63.829% 419.840 1 MobilenetV1/MobilenetV1/Conv2d_7_depthwise/depthwise_Fold 2018-05-07 23:30:49.739879: I tensorflow/core/util/stat_summarizer.cc:468] Add 25.851 0.120 0.123 0.305% 64.134% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_7_depthwise/add_fold 2018-05-07 23:30:49.739902: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 25.976 0.018 0.019 0.046% 64.180% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6 2018-05-07 23:30:49.739914: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 25.996 0.044 0.047 0.116% 64.296% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_7_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739925: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 26.045 1.401 1.342 3.319% 67.615% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Conv2D_Fold 2018-05-07 23:30:49.739951: I tensorflow/core/util/stat_summarizer.cc:468] Add 27.391 0.120 0.125 0.309% 67.924% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_7_pointwise/add_fold 2018-05-07 23:30:49.739966: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 27.518 0.019 0.019 0.047% 67.971% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6 2018-05-07 23:30:49.739978: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 27.538 0.046 0.047 0.115% 68.086% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_7_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.739989: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 27.587 0.447 0.490 1.212% 69.298% 419.840 1 MobilenetV1/MobilenetV1/Conv2d_8_depthwise/depthwise_Fold 2018-05-07 23:30:49.740008: I tensorflow/core/util/stat_summarizer.cc:468] Add 28.080 0.119 0.124 0.307% 69.605% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_8_depthwise/add_fold 2018-05-07 23:30:49.740019: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 28.206 0.018 0.019 0.046% 69.651% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6 2018-05-07 23:30:49.740031: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 28.226 0.045 0.047 0.117% 69.768% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_8_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740049: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 28.276 1.288 1.347 3.333% 73.101% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740061: I tensorflow/core/util/stat_summarizer.cc:468] Add 29.627 0.120 0.125 0.308% 73.409% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_8_pointwise/add_fold 2018-05-07 23:30:49.740072: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 29.754 0.019 0.019 0.047% 73.456% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6 2018-05-07 23:30:49.740084: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 29.775 0.045 0.047 0.115% 73.571% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_8_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740098: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 29.824 0.476 0.487 1.206% 74.777% 419.840 1 MobilenetV1/MobilenetV1/Conv2d_9_depthwise/depthwise_Fold 2018-05-07 23:30:49.740110: I tensorflow/core/util/stat_summarizer.cc:468] Add 30.314 0.119 0.123 0.305% 75.081% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_9_depthwise/add_fold 2018-05-07 23:30:49.740121: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 30.439 0.018 0.019 0.046% 75.127% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6 2018-05-07 23:30:49.740130: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 30.459 0.045 0.047 0.115% 75.243% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_9_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740141: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 30.508 1.367 1.339 3.313% 78.556% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740166: I tensorflow/core/util/stat_summarizer.cc:468] Add 31.852 0.120 0.125 0.309% 78.865% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_9_pointwise/add_fold 2018-05-07 23:30:49.740179: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 31.979 0.018 0.019 0.047% 78.912% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6 2018-05-07 23:30:49.740190: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 31.999 0.045 0.046 0.114% 79.026% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_9_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740200: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 32.048 0.453 0.483 1.195% 80.221% 419.840 1 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise_Fold 2018-05-07 23:30:49.740210: I tensorflow/core/util/stat_summarizer.cc:468] Add 32.534 0.120 0.124 0.307% 80.528% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/add_fold 2018-05-07 23:30:49.740222: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 32.660 0.019 0.019 0.046% 80.574% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6 2018-05-07 23:30:49.740233: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 32.679 0.045 0.047 0.115% 80.689% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_10_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740244: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 32.729 1.431 1.330 3.291% 83.980% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740255: I tensorflow/core/util/stat_summarizer.cc:468] Add 34.064 0.122 0.125 0.309% 84.290% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/add_fold 2018-05-07 23:30:49.740265: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 34.190 0.020 0.019 0.047% 84.336% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6 2018-05-07 23:30:49.740296: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 34.210 0.046 0.046 0.115% 84.451% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740309: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 34.259 0.451 0.481 1.190% 85.641% 419.840 1 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/depthwise_Fold 2018-05-07 23:30:49.740321: I tensorflow/core/util/stat_summarizer.cc:468] Add 34.743 0.119 0.124 0.307% 85.948% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/add_fold 2018-05-07 23:30:49.740331: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 34.869 0.019 0.019 0.046% 85.994% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6 2018-05-07 23:30:49.740414: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 34.889 0.053 0.046 0.115% 86.108% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_11_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740432: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 34.938 1.363 1.338 3.310% 89.419% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740443: I tensorflow/core/util/stat_summarizer.cc:468] Add 36.280 0.120 0.125 0.309% 89.727% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/add_fold 2018-05-07 23:30:49.740455: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 36.407 0.018 0.019 0.047% 89.774% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6 2018-05-07 23:30:49.740466: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 36.427 0.045 0.046 0.114% 89.889% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740481: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 36.476 0.122 0.129 0.318% 90.207% 118.784 1 MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise_Fold 2018-05-07 23:30:49.740492: I tensorflow/core/util/stat_summarizer.cc:468] Add 36.607 0.032 0.033 0.082% 90.289% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_12_depthwise/add_fold 2018-05-07 23:30:49.740504: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 36.642 0.006 0.005 0.012% 90.301% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6 2018-05-07 23:30:49.740514: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 36.648 0.015 0.014 0.035% 90.337% 100.352 1 MobilenetV1/MobilenetV1/Conv2d_12_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740525: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 36.664 1.020 1.013 2.506% 92.843% 200.704 1 MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740540: I tensorflow/core/util/stat_summarizer.cc:468] Add 37.682 0.063 0.066 0.164% 93.007% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_12_pointwise/add_fold 2018-05-07 23:30:49.740563: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 37.750 0.009 0.010 0.024% 93.031% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6 2018-05-07 23:30:49.740575: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 37.761 0.026 0.028 0.069% 93.100% 200.704 1 MobilenetV1/MobilenetV1/Conv2d_12_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740586: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 37.792 0.247 0.257 0.635% 93.735% 237.568 1 MobilenetV1/MobilenetV1/Conv2d_13_depthwise/depthwise_Fold 2018-05-07 23:30:49.740596: I tensorflow/core/util/stat_summarizer.cc:468] Add 38.051 0.061 0.064 0.159% 93.893% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_13_depthwise/add_fold 2018-05-07 23:30:49.740650: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 38.117 0.009 0.009 0.023% 93.916% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6 2018-05-07 23:30:49.740665: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 38.127 0.025 0.026 0.063% 93.979% 200.704 1 MobilenetV1/MobilenetV1/Conv2d_13_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740675: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 38.155 1.818 1.912 4.731% 98.710% 200.704 1 MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740685: I tensorflow/core/util/stat_summarizer.cc:468] Add 40.075 0.064 0.067 0.165% 98.875% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_13_pointwise/add_fold 2018-05-07 23:30:49.740697: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 40.143 0.009 0.010 0.024% 98.900% 0.000 1 MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6 2018-05-07 23:30:49.740709: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 40.154 0.028 0.030 0.073% 98.973% 200.704 1 MobilenetV1/MobilenetV1/Conv2d_13_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740720: I tensorflow/core/util/stat_summarizer.cc:468] AvgPool 40.187 0.014 0.015 0.037% 99.010% 4.096 1 MobilenetV1/Logits/AvgPool_1a/AvgPool 2018-05-07 23:30:49.740731: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 40.203 0.330 0.346 0.857% 99.867% 4.004 1 MobilenetV1/Logits/Conv2d_1c_1x1/Conv2D 2018-05-07 23:30:49.740742: I tensorflow/core/util/stat_summarizer.cc:468] BiasAdd 40.553 0.005 0.005 0.013% 99.880% 0.000 1 MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd 2018-05-07 23:30:49.740752: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 40.559 0.005 0.006 0.014% 99.894% 4.004 1 MobilenetV1/Logits/Conv2d_1c_1x1/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.740763: I tensorflow/core/util/stat_summarizer.cc:468] Squeeze 40.567 0.004 0.004 0.011% 99.905% 0.000 1 MobilenetV1/Logits/SpatialSqueeze 2018-05-07 23:30:49.740774: I tensorflow/core/util/stat_summarizer.cc:468] Shape 40.582 0.003 0.004 0.009% 99.914% 0.008 1 MobilenetV1/Predictions/Shape 2018-05-07 23:30:49.740785: I tensorflow/core/util/stat_summarizer.cc:468] Softmax 40.587 0.022 0.026 0.065% 99.979% 0.000 1 MobilenetV1/Predictions/Softmax 2018-05-07 23:30:49.740808: I tensorflow/core/util/stat_summarizer.cc:468] Reshape 40.615 0.003 0.003 0.008% 99.987% 0.000 1 MobilenetV1/Predictions/Reshape_1 2018-05-07 23:30:49.740819: I tensorflow/core/util/stat_summarizer.cc:468] _Retval 40.619 0.004 0.005 0.013% 100.000% 0.000 1 _retval_MobilenetV1/Predictions/Reshape_1_0_0 2018-05-07 23:30:49.740828: I tensorflow/core/util/stat_summarizer.cc:468] 2018-05-07 23:30:49.740841: I tensorflow/core/util/stat_summarizer.cc:468] ============================== Top by Computation Time ============================== 2018-05-07 23:30:49.740851: I tensorflow/core/util/stat_summarizer.cc:468] [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] 2018-05-07 23:30:49.740864: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 0.153 2.982 3.152 7.797% 7.797% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_0/Conv2D_Fold 2018-05-07 23:30:49.740874: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 4.211 2.591 2.780 6.879% 14.676% 1606.784 1 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise_Fold 2018-05-07 23:30:49.740883: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 14.136 2.029 2.060 5.097% 19.773% 1610.240 1 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/depthwise_Fold 2018-05-07 23:30:49.740893: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 38.155 1.818 1.912 4.731% 24.503% 200.704 1 MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740904: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 16.975 1.379 1.484 3.672% 28.175% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740915: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 28.276 1.288 1.347 3.333% 31.508% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740925: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 26.045 1.401 1.342 3.319% 34.827% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740940: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 30.508 1.367 1.339 3.313% 38.140% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740951: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 34.938 1.363 1.338 3.310% 41.451% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740962: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 32.729 1.431 1.330 3.291% 44.742% 401.408 1 MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Conv2D_Fold 2018-05-07 23:30:49.740971: I tensorflow/core/util/stat_summarizer.cc:468] 2018-05-07 23:30:49.740981: I tensorflow/core/util/stat_summarizer.cc:468] ============================== Top by Memory Use ============================== 2018-05-07 23:30:49.740990: I tensorflow/core/util/stat_summarizer.cc:468] [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] 2018-05-07 23:30:49.741001: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 10.200 0.895 0.729 1.803% 1.803% 3211.264 1 MobilenetV1/MobilenetV1/Conv2d_1_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.741015: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 7.795 0.915 1.216 3.008% 4.811% 3211.264 1 MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Conv2D_Fold 2018-05-07 23:30:49.741026: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 14.136 2.029 2.060 5.097% 9.908% 1610.240 1 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/depthwise_Fold 2018-05-07 23:30:49.741038: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 4.211 2.591 2.780 6.879% 16.787% 1606.784 1 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise_Fold 2018-05-07 23:30:49.741049: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 16.975 1.379 1.484 3.672% 20.459% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Conv2D_Fold 2018-05-07 23:30:49.741061: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 16.778 0.179 0.192 0.474% 20.933% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_3_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.741072: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 13.933 0.183 0.198 0.490% 21.423% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_2_pointwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.741083: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 12.552 0.811 0.806 1.995% 23.417% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Conv2D_Fold 2018-05-07 23:30:49.741108: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 7.570 0.175 0.221 0.546% 23.963% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_1_depthwise/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.741120: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 3.892 0.191 0.314 0.776% 24.739% 1605.632 1 MobilenetV1/MobilenetV1/Conv2d_0/act_quant/FakeQuantWithMinMaxVars 2018-05-07 23:30:49.741132: I tensorflow/core/util/stat_summarizer.cc:468] 2018-05-07 23:30:49.741143: I tensorflow/core/util/stat_summarizer.cc:468] ============================== Summary by node type ============================== 2018-05-07 23:30:49.741155: I tensorflow/core/util/stat_summarizer.cc:468] [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called] 2018-05-07 23:30:49.741178: I tensorflow/core/util/stat_summarizer.cc:468] Conv2D 15 19.303 47.859% 47.859% 12447.652 15 2018-05-07 23:30:49.741190: I tensorflow/core/util/stat_summarizer.cc:468] DepthwiseConv2dNative 13 10.639 26.378% 74.237% 7905.664 13 2018-05-07 23:30:49.741200: I tensorflow/core/util/stat_summarizer.cc:468] Add 27 6.211 15.399% 89.636% 0.000 27 2018-05-07 23:30:49.741223: I tensorflow/core/util/stat_summarizer.cc:468] FakeQuantWithMinMaxVars 28 2.899 7.188% 96.824% 20174.756 28 2018-05-07 23:30:49.741234: I tensorflow/core/util/stat_summarizer.cc:468] Relu6 27 0.980 2.430% 99.254% 0.000 27 2018-05-07 23:30:49.741244: I tensorflow/core/util/stat_summarizer.cc:468] Const 60 0.121 0.300% 99.554% 0.000 60 2018-05-07 23:30:49.741253: I tensorflow/core/util/stat_summarizer.cc:468] NoOp 1 0.116 0.288% 99.841% 0.000 1 2018-05-07 23:30:49.741263: I tensorflow/core/util/stat_summarizer.cc:468] Softmax 1 0.026 0.064% 99.906% 0.000 1 2018-05-07 23:30:49.741280: I tensorflow/core/util/stat_summarizer.cc:468] AvgPool 1 0.015 0.037% 99.943% 4.096 1 2018-05-07 23:30:49.741292: I tensorflow/core/util/stat_summarizer.cc:468] _Retval 1 0.005 0.012% 99.955% 0.000 1 2018-05-07 23:30:49.741303: I tensorflow/core/util/stat_summarizer.cc:468] BiasAdd 1 0.005 0.012% 99.968% 0.000 1 2018-05-07 23:30:49.741314: I tensorflow/core/util/stat_summarizer.cc:468] Squeeze 1 0.004 0.010% 99.978% 0.000 1 2018-05-07 23:30:49.741324: I tensorflow/core/util/stat_summarizer.cc:468] _Arg 1 0.003 0.007% 99.985% 0.000 1 2018-05-07 23:30:49.741335: I tensorflow/core/util/stat_summarizer.cc:468] Shape 1 0.003 0.007% 99.993% 0.008 1 2018-05-07 23:30:49.741346: I tensorflow/core/util/stat_summarizer.cc:468] Reshape 1 0.003 0.007% 100.000% 0.000 1 2018-05-07 23:30:49.741356: I tensorflow/core/util/stat_summarizer.cc:468] 2018-05-07 23:30:49.741371: I tensorflow/core/util/stat_summarizer.cc:468] Timings (microseconds): count=246 first=39108 curr=39629 min=37232 max=70179 avg=40420.5 std=5013 2018-05-07 23:30:49.741382: I tensorflow/core/util/stat_summarizer.cc:468] Memory (bytes): count=246 first=40532176 curr=40532176 min=40532176 max=40536180 avg=4.05322e+07 std=359 2018-05-07 23:30:49.741396: I tensorflow/core/util/stat_summarizer.cc:468] 179 nodes observed 2018-05-07 23:30:49.741408: I tensorflow/core/util/stat_summarizer.cc:468] 2018-05-07 23:30:50.044874: I tensorflow/tools/benchmark/benchmark_model.cc:631] FLOPs estimate: 1.14B 2018-05-07 23:30:50.044925: I tensorflow/tools/benchmark/benchmark_model.cc:633] FLOPs/second: 29.87B