dev board: M1 Asahi Linux - YingkunZhou/EdgeTransformerBench GitHub Wiki
we default use llvm-16 to build dnn lib
M1 firestorm @ 1 thread @ 3.0GHz fp32
INFO: Using CPU backend
INFO: Using num_threads == 1
Creating ncnn net: efficientformerv2_s0
opt status: 111011101 ==> 000000001
(index: 985, score: 11.782274), (index: 644, score: 4.856472), (index: 108, score: 3.921541),
[450 iters] min = 44.46ms max = 44.57ms median = 44.51ms mean = 44.51ms
Creating ncnn net: efficientformerv2_s1
opt status: 111011101 ==> 000000001
(index: 985, score: 13.111187), (index: 89, score: 4.181216), (index: 984, score: 4.101791),
[301 iters] min = 66.49ms max = 66.60ms median = 66.54ms mean = 66.54ms
Creating ncnn net: efficientformerv2_s2
opt status: 111011101 ==> 000000001
(index: 985, score: 12.502236), (index: 309, score: 3.697202), (index: 22, score: 3.686200),
[188 iters] min = 106.79ms max = 106.96ms median = 106.87ms mean = 106.87ms
Creating ncnn net: mobilevitv2_050
opt status: 111011101 ==> 000000001
(index: 985, score: 8.305047), (index: 309, score: 2.612882), (index: 584, score: 2.330211),
[1238 iters] min = 16.14ms max = 16.19ms median = 16.16ms mean = 16.16ms
Creating ncnn net: mobilevitv2_075
opt status: 111011101 ==> 000000001
(index: 985, score: 8.126369), (index: 309, score: 2.389992), (index: 308, score: 1.885909),
[637 iters] min = 31.38ms max = 31.50ms median = 31.43ms mean = 31.43ms
Creating ncnn net: mobilevitv2_100
opt status: 111011101 ==> 000000001
(index: 985, score: 8.254770), (index: 557, score: 2.225806), (index: 309, score: 1.942585),
[389 iters] min = 51.39ms max = 51.57ms median = 51.48ms mean = 51.48ms
Creating ncnn net: mobilevitv2_125
opt status: 111011101 ==> 000000001
(index: 985, score: 8.281292), (index: 309, score: 1.960417), (index: 883, score: 1.290664),
[261 iters] min = 76.74ms max = 76.94ms median = 76.82ms mean = 76.83ms
Creating ncnn net: mobilevitv2_150
opt status: 111011101 ==> 000000001
(index: 985, score: 9.099491), (index: 308, score: 2.251916), (index: 301, score: 2.153155),
[188 iters] min = 106.32ms max = 106.92ms median = 106.40ms mean = 106.45ms
Creating ncnn net: mobilevitv2_175
opt status: 111011101 ==> 000000001
(index: 985, score: 8.900545), (index: 494, score: 2.110986), (index: 309, score: 1.876236),
[141 iters] min = 142.03ms max = 142.24ms median = 142.09ms mean = 142.11ms
Creating ncnn net: mobilevitv2_200
opt status: 111011101 ==> 000000001
(index: 985, score: 8.531105), (index: 883, score: 2.244246), (index: 309, score: 2.230253),
[112 iters] min = 179.89ms max = 180.11ms median = 179.95ms mean = 179.96ms
Creating ncnn net: mobilevit_xx_small
opt status: 111011101 ==> 000000001
(index: 378, score: 6.810028), (index: 382, score: 6.278749), (index: 19, score: 6.213943),
[2790 iters] min = 7.14ms max = 7.21ms median = 7.17ms mean = 7.17ms
Creating ncnn net: resnet50
opt status: 111011101 ==> 000000001
(index: 985, score: 7.484058), (index: 113, score: -4.938162), (index: 310, score: -5.258441),
[236 iters] min = 84.75ms max = 84.87ms median = 84.80ms mean = 84.80ms
Creating ncnn net: mobilenetv3_large_100
opt status: 111011101 ==> 000000001
(index: 985, score: 9.600927), (index: 308, score: 2.362727), (index: 310, score: 2.348943),
[2899 iters] min = 6.87ms max = 7.03ms median = 6.90ms mean = 6.90ms
Creating ncnn net: tf_efficientnetv2_b0
opt status: 111011101 ==> 000000001
(index: 985, score: 9.552636), (index: 309, score: 2.377687), (index: 108, score: 2.288832),
[1146 iters] min = 17.37ms max = 17.54ms median = 17.48ms mean = 17.47ms
Creating ncnn net: tf_efficientnetv2_b1
opt status: 111011101 ==> 000000001
(index: 985, score: 9.484985), (index: 861, score: 2.249804), (index: 309, score: 2.138905),
[775 iters] min = 25.74ms max = 25.94ms median = 25.83ms mean = 25.83ms
Creating ncnn net: tf_efficientnetv2_b2
opt status: 111011101 ==> 000000001
(index: 985, score: 9.816031), (index: 883, score: 2.518352), (index: 113, score: 2.038452),
[542 iters] min = 36.86ms max = 36.95ms median = 36.90ms mean = 36.90ms
Creating ncnn net: tf_efficientnetv2_b3
opt status: 111011101 ==> 000000001
(index: 985, score: 9.093819), (index: 955, score: 2.889793), (index: 947, score: 2.188509),
[314 iters] min = 63.75ms max = 64.02ms median = 63.87ms mean = 63.87msM1 firestorm @ 1 thread @ 3.0GHz fp16 (default)
$ MODEL=ALL make run-ncnn-perf
INFO: Using CPU backend
INFO: Using num_threads == 1
Creating ncnn net: efficientformerv2_s0
(index: 985, score: 11.859375), (index: 644, score: 4.875000), (index: 108, score: 4.003906),
[390 iters] min = 51.28ms max = 51.48ms median = 51.33ms mean = 51.33ms
Creating ncnn net: efficientformerv2_s1
(index: 985, score: 12.976562), (index: 308, score: 4.152344), (index: 984, score: 4.132812),
[260 iters] min = 76.93ms max = 77.05ms median = 76.98ms mean = 76.98ms
Creating ncnn net: efficientformerv2_s2
(index: 985, score: 12.453125), (index: 22, score: 3.632812), (index: 309, score: 3.628906),
[166 iters] min = 120.80ms max = 120.99ms median = 120.91ms mean = 120.91ms
SwiftFormer_XS model doesn't exist!!!
SwiftFormer_S model doesn't exist!!!
SwiftFormer_L1 model doesn't exist!!!
EMO_1M model doesn't exist!!!
EMO_2M model doesn't exist!!!
EMO_5M model doesn't exist!!!
EMO_6M model doesn't exist!!!
edgenext_xx_small model doesn't exist!!!
edgenext_x_small model doesn't exist!!!
edgenext_small model doesn't exist!!!
Creating ncnn net: mobilevitv2_050
(index: 985, score: 8.296875), (index: 309, score: 2.617188), (index: 584, score: 2.343750),
[1695 iters] min = 11.79ms max = 11.85ms median = 11.80ms mean = 11.80ms
Creating ncnn net: mobilevitv2_075
(index: 985, score: 8.117188), (index: 309, score: 2.384766), (index: 308, score: 1.889648),
[878 iters] min = 22.76ms max = 22.81ms median = 22.78ms mean = 22.78ms
Creating ncnn net: mobilevitv2_100
(index: 985, score: 8.242188), (index: 557, score: 2.226562), (index: 309, score: 1.937500),
[537 iters] min = 37.18ms max = 37.30ms median = 37.25ms mean = 37.25ms
Creating ncnn net: mobilevitv2_125
(index: 985, score: 8.281250), (index: 309, score: 1.969727), (index: 883, score: 1.276367),
[365 iters] min = 54.74ms max = 54.88ms median = 54.81ms mean = 54.81ms
Creating ncnn net: mobilevitv2_150
(index: 985, score: 9.070312), (index: 308, score: 2.230469), (index: 301, score: 2.130859),
[264 iters] min = 75.84ms max = 75.93ms median = 75.89ms mean = 75.89ms
Creating ncnn net: mobilevitv2_175
(index: 985, score: 8.882812), (index: 494, score: 2.103516), (index: 309, score: 1.873047),
[200 iters] min = 100.17ms max = 100.28ms median = 100.23ms mean = 100.23ms
Creating ncnn net: mobilevitv2_200
(index: 985, score: 8.500000), (index: 883, score: 2.234375), (index: 309, score: 2.230469),
[155 iters] min = 129.04ms max = 129.26ms median = 129.14ms mean = 129.13ms
Creating ncnn net: mobilevit_xx_small
(index: 999, score: -nan), (index: 998, score: -nan), (index: 997, score: -nan),
[4044 iters] min = 4.94ms max = 4.96ms median = 4.95ms mean = 4.95ms
mobilevit_x_small model doesn't exist!!!
mobilevit_small model doesn't exist!!!
LeViT_128S model doesn't exist!!!
LeViT_128 model doesn't exist!!!
LeViT_192 model doesn't exist!!!
LeViT_256 model doesn't exist!!!
Creating ncnn net: resnet50
(index: 985, score: 7.359375), (index: 113, score: -4.937500), (index: 310, score: -5.242188),
[323 iters] min = 61.94ms max = 62.03ms median = 61.99ms mean = 61.99ms
Creating ncnn net: mobilenetv3_large_100
(index: 985, score: 9.640625), (index: 308, score: 2.371094), (index: 310, score: 2.343750),
[3335 iters] min = 5.98ms max = 6.05ms median = 6.00ms mean = 6.00ms
Creating ncnn net: tf_efficientnetv2_b0
(index: 985, score: 9.562500), (index: 309, score: 2.359375), (index: 108, score: 2.283203),
[1316 iters] min = 15.17ms max = 15.23ms median = 15.20ms mean = 15.20ms
Creating ncnn net: tf_efficientnetv2_b1
(index: 985, score: 9.484375), (index: 861, score: 2.244141), (index: 309, score: 2.140625),
[917 iters] min = 21.78ms max = 21.85ms median = 21.81ms mean = 21.81ms
Creating ncnn net: tf_efficientnetv2_b2
(index: 985, score: 9.828125), (index: 883, score: 2.531250), (index: 113, score: 2.039062),
[634 iters] min = 31.55ms max = 31.63ms median = 31.59ms mean = 31.59ms
Creating ncnn net: tf_efficientnetv2_b3
(index: 985, score: 9.109375), (index: 955, score: 2.888672), (index: 947, score: 2.191406),
[362 iters] min = 55.33ms max = 55.46ms median = 55.40ms mean = 55.40msM1 firestorm @ 1 thread @ 3.0GHz int8 w/kl-mod
INFO: Using CPU backend
INFO: Using num_threads == 1
Creating ncnn net: efficientformerv2_s0
(index: 985, score: 11.296875), (index: 108, score: 6.078125), (index: 644, score: 5.914062),
[437 iters] min = 45.82ms max = 46.70ms median = 45.86ms mean = 45.87ms
Creating ncnn net: efficientformerv2_s1
(index: 898, score: 8.937500), (index: 563, score: 6.921875), (index: 27, score: 6.207031),
[296 iters] min = 67.58ms max = 67.70ms median = 67.64ms mean = 67.64ms
Creating ncnn net: efficientformerv2_s2
(index: 985, score: 12.578125), (index: 883, score: 4.468750), (index: 309, score: 4.156250),
[193 iters] min = 103.97ms max = 104.15ms median = 104.05ms mean = 104.05ms
Creating ncnn net: mobilevitv2_050
(index: 985, score: 10.930462), (index: 506, score: 6.182128), (index: 584, score: 5.944051),
[1799 iters] min = 11.09ms max = 11.20ms median = 11.12ms mean = 11.12ms
Creating ncnn net: mobilevitv2_075
(index: 985, score: 10.644029), (index: 584, score: 3.415187), (index: 309, score: 3.292385),
[1094 iters] min = 18.25ms max = 18.33ms median = 18.29ms mean = 18.29ms
Creating ncnn net: mobilevitv2_100
(index: 985, score: 12.304844), (index: 309, score: 4.603567), (index: 584, score: 3.558692),
[750 iters] min = 26.65ms max = 26.70ms median = 26.67ms mean = 26.67ms
Creating ncnn net: mobilevitv2_125
(index: 985, score: 16.227858), (index: 309, score: 5.082381), (index: 738, score: 3.812296),
[553 iters] min = 36.18ms max = 36.25ms median = 36.20ms mean = 36.20ms
Creating ncnn net: mobilevitv2_150
(index: 985, score: 9.671791), (index: 309, score: 3.081745), (index: 308, score: 2.859699),
[428 iters] min = 46.80ms max = 46.88ms median = 46.83ms mean = 46.83ms
Creating ncnn net: mobilevitv2_175
(index: 985, score: 9.482336), (index: 681, score: 2.202604), (index: 309, score: 2.173967),
[341 iters] min = 58.64ms max = 58.74ms median = 58.69ms mean = 58.69ms
Creating ncnn net: mobilevitv2_200
(index: 985, score: 11.824309), (index: 883, score: 4.446024), (index: 309, score: 4.375680),
[279 iters] min = 71.65ms max = 71.77ms median = 71.70ms mean = 71.70ms
Creating ncnn net: mobilevit_xx_small
(index: 999, score: nan), (index: 998, score: nan), (index: 997, score: nan),
[3791 iters] min = 5.27ms max = 5.30ms median = 5.28ms mean = 5.28ms
Creating ncnn net: resnet50
(index: 985, score: 6.716446), (index: 113, score: -4.833062), (index: 310, score: -5.049811),
[647 iters] min = 30.86ms max = 31.01ms median = 30.92ms mean = 30.92ms
Creating ncnn net: mobilenetv3_large_100
(index: 985, score: 10.202103), (index: 883, score: 2.712523), (index: 533, score: 2.558361),
[3412 iters] min = 5.85ms max = 5.93ms median = 5.86ms mean = 5.86ms
Creating ncnn net: tf_efficientnetv2_b0
(index: 985, score: 13.037141), (index: 108, score: 3.455601), (index: 309, score: 3.431606),
[1655 iters] min = 12.08ms max = 12.15ms median = 12.09ms mean = 12.09ms
Creating ncnn net: tf_efficientnetv2_b1
(index: 985, score: 12.271066), (index: 309, score: 3.071457), (index: 995, score: 2.809345),
[1088 iters] min = 18.36ms max = 18.46ms median = 18.39ms mean = 18.39ms
Creating ncnn net: tf_efficientnetv2_b2
(index: 985, score: 11.739453), (index: 309, score: 3.525121), (index: 883, score: 3.265590),
[788 iters] min = 25.38ms max = 25.50ms median = 25.40ms mean = 25.40ms
Creating ncnn net: tf_efficientnetv2_b3
(index: 985, score: 9.467087), (index: 947, score: 2.215796), (index: 955, score: 2.111230),
[466 iters] min = 42.93ms max = 43.09ms median = 42.96ms mean = 42.96msM1 firestorm @ 1 thread @ 3.0GHz fp32 threads==4
INFO: Using CPU backend
INFO: Using num_threads == 4
Creating ncnn net: efficientformerv2_s0
opt status: 111011101 ==> 000000001
(index: 985, score: 11.782274), (index: 644, score: 4.856472), (index: 108, score: 3.921541),
[1561 iters] min = 12.78ms max = 12.89ms median = 12.81ms mean = 12.81ms
Creating ncnn net: efficientformerv2_s1
opt status: 111011101 ==> 000000001
(index: 985, score: 13.111187), (index: 89, score: 4.181216), (index: 984, score: 4.101791),
[1067 iters] min = 18.73ms max = 18.80ms median = 18.76ms mean = 18.76ms
Creating ncnn net: efficientformerv2_s2
opt status: 111011101 ==> 000000001
(index: 985, score: 12.502236), (index: 309, score: 3.697202), (index: 22, score: 3.686200),
[659 iters] min = 30.32ms max = 30.41ms median = 30.36ms mean = 30.36ms
Creating ncnn net: mobilevitv2_050
opt status: 111011101 ==> 000000001
(index: 985, score: 8.305047), (index: 309, score: 2.612882), (index: 584, score: 2.330211),
[3531 iters] min = 5.62ms max = 5.72ms median = 5.66ms mean = 5.67ms
Creating ncnn net: mobilevitv2_075
opt status: 111011101 ==> 000000001
(index: 985, score: 8.126369), (index: 309, score: 2.389992), (index: 308, score: 1.885909),
[1906 iters] min = 10.46ms max = 10.54ms median = 10.49ms mean = 10.50ms
Creating ncnn net: mobilevitv2_100
opt status: 111011101 ==> 000000001
(index: 985, score: 8.254770), (index: 557, score: 2.225806), (index: 309, score: 1.942585),
[1227 iters] min = 16.24ms max = 16.37ms median = 16.31ms mean = 16.31ms
Creating ncnn net: mobilevitv2_125
opt status: 111011101 ==> 000000001
(index: 985, score: 8.281292), (index: 309, score: 1.960417), (index: 883, score: 1.290664),
[790 iters] min = 25.27ms max = 25.48ms median = 25.33ms mean = 25.33ms
Creating ncnn net: mobilevitv2_150
opt status: 111011101 ==> 000000001
(index: 985, score: 9.099491), (index: 308, score: 2.251916), (index: 301, score: 2.153155),
[623 iters] min = 32.03ms max = 32.20ms median = 32.11ms mean = 32.11ms
Creating ncnn net: mobilevitv2_175
opt status: 111011101 ==> 000000001
(index: 985, score: 8.900545), (index: 494, score: 2.110986), (index: 309, score: 1.876236),
[442 iters] min = 45.22ms max = 45.36ms median = 45.28ms mean = 45.28ms
Creating ncnn net: mobilevitv2_200
opt status: 111011101 ==> 000000001
(index: 985, score: 8.531105), (index: 883, score: 2.244246), (index: 309, score: 2.230253),
[380 iters] min = 52.62ms max = 52.74ms median = 52.69ms mean = 52.69ms
Creating ncnn net: mobilevit_xx_small
opt status: 111011101 ==> 000000001
(index: 999, score: -nan), (index: 998, score: -nan), (index: 997, score: -nan),
[6311 iters] min = 3.15ms max = 3.20ms median = 3.17ms mean = 3.17ms
Creating ncnn net: resnet50
opt status: 111011101 ==> 000000001
(index: 985, score: 7.484058), (index: 113, score: -4.938162), (index: 310, score: -5.258441),
[808 iters] min = 24.73ms max = 24.84ms median = 24.77ms mean = 24.77ms
Creating ncnn net: mobilenetv3_large_100
opt status: 111011101 ==> 000000001
(index: 985, score: 9.600927), (index: 308, score: 2.362727), (index: 310, score: 2.348943),
[7127 iters] min = 2.79ms max = 2.84ms median = 2.81ms mean = 2.81ms
Creating ncnn net: tf_efficientnetv2_b0
opt status: 111011101 ==> 000000001
(index: 985, score: 9.552636), (index: 309, score: 2.377687), (index: 108, score: 2.288832),
[3608 iters] min = 5.51ms max = 5.63ms median = 5.54ms mean = 5.54ms
Creating ncnn net: tf_efficientnetv2_b1
opt status: 111011101 ==> 000000001
(index: 985, score: 9.484985), (index: 861, score: 2.249804), (index: 309, score: 2.138905),
[2525 iters] min = 7.89ms max = 7.96ms median = 7.92ms mean = 7.92ms
Creating ncnn net: tf_efficientnetv2_b2
opt status: 111011101 ==> 000000001
(index: 985, score: 9.816031), (index: 883, score: 2.518352), (index: 113, score: 2.038452),
[1761 iters] min = 11.32ms max = 11.43ms median = 11.36ms mean = 11.36ms
Creating ncnn net: tf_efficientnetv2_b3
opt status: 111011101 ==> 000000001
(index: 985, score: 9.093819), (index: 955, score: 2.889793), (index: 947, score: 2.188509),
[981 iters] min = 20.34ms max = 20.47ms median = 20.41ms mean = 20.40msM1 firestorm @ 1 thread @ 3.0GHz fp16 threads==4
INFO: Using CPU backend
INFO: Using num_threads == 4
Creating ncnn net: efficientformerv2_s0
(index: 985, score: 11.859375), (index: 644, score: 4.875000), (index: 108, score: 4.003906),
[1349 iters] min = 14.81ms max = 14.90ms median = 14.84ms mean = 14.84ms
Creating ncnn net: efficientformerv2_s1
(index: 985, score: 12.976562), (index: 308, score: 4.152344), (index: 984, score: 4.132812),
[916 iters] min = 21.81ms max = 21.86ms median = 21.84ms mean = 21.84ms
Creating ncnn net: efficientformerv2_s2
(index: 985, score: 12.453125), (index: 22, score: 3.632812), (index: 309, score: 3.628906),
[585 iters] min = 34.17ms max = 34.24ms median = 34.20ms mean = 34.20ms
SwiftFormer_XS model doesn't exist!!!
SwiftFormer_S model doesn't exist!!!
SwiftFormer_L1 model doesn't exist!!!
EMO_1M model doesn't exist!!!
EMO_2M model doesn't exist!!!
EMO_5M model doesn't exist!!!
EMO_6M model doesn't exist!!!
edgenext_xx_small model doesn't exist!!!
edgenext_x_small model doesn't exist!!!
edgenext_small model doesn't exist!!!
Creating ncnn net: mobilevitv2_050
(index: 985, score: 8.296875), (index: 309, score: 2.617188), (index: 584, score: 2.343750),
[4722 iters] min = 4.21ms max = 4.28ms median = 4.24ms mean = 4.24ms
Creating ncnn net: mobilevitv2_075
(index: 985, score: 8.117188), (index: 309, score: 2.384766), (index: 308, score: 1.889648),
[2616 iters] min = 7.63ms max = 7.68ms median = 7.65ms mean = 7.65ms
Creating ncnn net: mobilevitv2_100
(index: 985, score: 8.242188), (index: 557, score: 2.226562), (index: 309, score: 1.937500),
[1710 iters] min = 11.67ms max = 11.74ms median = 11.70ms mean = 11.70ms
Creating ncnn net: mobilevitv2_125
(index: 985, score: 8.281250), (index: 309, score: 1.969727), (index: 883, score: 1.276367),
[1174 iters] min = 17.00ms max = 17.08ms median = 17.04ms mean = 17.04ms
Creating ncnn net: mobilevitv2_150
(index: 985, score: 9.070312), (index: 308, score: 2.230469), (index: 301, score: 2.130859),
[878 iters] min = 22.74ms max = 22.84ms median = 22.78ms mean = 22.78ms
Creating ncnn net: mobilevitv2_175
(index: 985, score: 8.882812), (index: 494, score: 2.103516), (index: 309, score: 1.873047),
[669 iters] min = 29.87ms max = 29.96ms median = 29.92ms mean = 29.92ms
Creating ncnn net: mobilevitv2_200
(index: 985, score: 8.500000), (index: 883, score: 2.234375), (index: 309, score: 2.230469),
[539 iters] min = 37.12ms max = 37.22ms median = 37.17ms mean = 37.17ms
Creating ncnn net: mobilevit_xx_small
(index: 999, score: -nan), (index: 998, score: -nan), (index: 997, score: -nan),
[8197 iters] min = 2.43ms max = 2.46ms median = 2.44ms mean = 2.44ms
mobilevit_x_small model doesn't exist!!!
mobilevit_small model doesn't exist!!!
LeViT_128S model doesn't exist!!!
LeViT_128 model doesn't exist!!!
LeViT_192 model doesn't exist!!!
LeViT_256 model doesn't exist!!!
Creating ncnn net: resnet50
(index: 985, score: 7.359375), (index: 113, score: -4.937500), (index: 310, score: -5.242188),
[1216 iters] min = 16.43ms max = 16.48ms median = 16.45ms mean = 16.45ms
Creating ncnn net: mobilenetv3_large_100
(index: 985, score: 9.640625), (index: 308, score: 2.371094), (index: 310, score: 2.343750),
[9063 iters] min = 2.19ms max = 2.23ms median = 2.21ms mean = 2.21ms
Creating ncnn net: tf_efficientnetv2_b0
(index: 985, score: 9.562500), (index: 309, score: 2.359375), (index: 108, score: 2.283203),
[4269 iters] min = 4.67ms max = 4.71ms median = 4.68ms mean = 4.69ms
Creating ncnn net: tf_efficientnetv2_b1
(index: 985, score: 9.484375), (index: 861, score: 2.244141), (index: 309, score: 2.140625),
[2972 iters] min = 6.71ms max = 6.76ms median = 6.73ms mean = 6.73ms
Creating ncnn net: tf_efficientnetv2_b2
(index: 985, score: 9.828125), (index: 883, score: 2.531250), (index: 113, score: 2.039062),
[2106 iters] min = 9.48ms max = 9.52ms median = 9.50ms mean = 9.50ms
Creating ncnn net: tf_efficientnetv2_b3
(index: 985, score: 9.109375), (index: 955, score: 2.888672), (index: 947, score: 2.191406),
[1187 iters] min = 16.83ms max = 16.89ms median = 16.86ms mean = 16.86msM1 firestorm @ 1 thread @ 3.0GHz int8 threads==4 w/kl-mod
INFO: Using CPU backend
INFO: Using num_threads == 4
Creating ncnn net: efficientformerv2_s0
(index: 985, score: 11.296875), (index: 108, score: 6.078125), (index: 644, score: 5.914062),
[1508 iters] min = 13.25ms max = 13.29ms median = 13.27ms mean = 13.27ms
Creating ncnn net: efficientformerv2_s1
(index: 898, score: 8.937500), (index: 563, score: 6.921875), (index: 27, score: 6.207031),
[1040 iters] min = 19.21ms max = 19.30ms median = 19.24ms mean = 19.24ms
Creating ncnn net: efficientformerv2_s2
(index: 985, score: 12.578125), (index: 883, score: 4.468750), (index: 309, score: 4.156250),
[673 iters] min = 29.72ms max = 29.80ms median = 29.75ms mean = 29.75ms
Creating ncnn net: mobilevitv2_050
(index: 985, score: 10.930462), (index: 506, score: 6.182128), (index: 584, score: 5.944051),
[4733 iters] min = 4.14ms max = 4.34ms median = 4.23ms mean = 4.23ms
Creating ncnn net: mobilevitv2_075
(index: 985, score: 10.644029), (index: 584, score: 3.415187), (index: 309, score: 3.292385),
[2896 iters] min = 6.84ms max = 7.01ms median = 6.91ms mean = 6.91ms
Creating ncnn net: mobilevitv2_100
(index: 985, score: 12.304844), (index: 309, score: 4.603567), (index: 584, score: 3.558692),
[2097 iters] min = 9.45ms max = 9.66ms median = 9.53ms mean = 9.54ms
Creating ncnn net: mobilevitv2_125
(index: 985, score: 16.227858), (index: 309, score: 5.082381), (index: 738, score: 3.812296),
[1507 iters] min = 13.19ms max = 17.29ms median = 13.26ms mean = 13.27ms
Creating ncnn net: mobilevitv2_150
(index: 985, score: 9.671791), (index: 309, score: 3.081745), (index: 308, score: 2.859699),
[1212 iters] min = 16.38ms max = 21.26ms median = 16.49ms mean = 16.51ms
Creating ncnn net: mobilevitv2_175
(index: 985, score: 9.482336), (index: 681, score: 2.202604), (index: 309, score: 2.173967),
[953 iters] min = 20.87ms max = 25.03ms median = 20.98ms mean = 20.99ms
Creating ncnn net: mobilevitv2_200
(index: 985, score: 11.824309), (index: 883, score: 4.446024), (index: 309, score: 4.375680),
[818 iters] min = 24.32ms max = 31.35ms median = 24.44ms mean = 24.46ms
Creating ncnn net: mobilevit_xx_small
(index: 999, score: -nan), (index: 998, score: -nan), (index: 997, score: -nan),
[8233 iters] min = 2.42ms max = 2.46ms median = 2.43ms mean = 2.43ms
Creating ncnn net: resnet50
(index: 985, score: 6.716446), (index: 113, score: -4.833062), (index: 310, score: -5.049811),
[2106 iters] min = 9.47ms max = 9.62ms median = 9.50ms mean = 9.50ms
Creating ncnn net: mobilenetv3_large_100
(index: 985, score: 10.202103), (index: 883, score: 2.712523), (index: 533, score: 2.558361),
[9485 iters] min = 2.10ms max = 2.13ms median = 2.11ms mean = 2.11ms
Creating ncnn net: tf_efficientnetv2_b0
(index: 985, score: 13.037141), (index: 108, score: 3.455601), (index: 309, score: 3.431606),
[4942 iters] min = 4.03ms max = 4.09ms median = 4.05ms mean = 4.05ms
Creating ncnn net: tf_efficientnetv2_b1
(index: 985, score: 12.271066), (index: 309, score: 3.071457), (index: 995, score: 2.809345),
[3286 iters] min = 6.02ms max = 6.14ms median = 6.11ms mean = 6.09ms
Creating ncnn net: tf_efficientnetv2_b2
(index: 985, score: 11.739453), (index: 309, score: 3.525121), (index: 883, score: 3.265590),
[2459 iters] min = 8.10ms max = 8.19ms median = 8.14ms mean = 8.13ms
Creating ncnn net: tf_efficientnetv2_b3
(index: 985, score: 9.467087), (index: 947, score: 2.215796), (index: 955, score: 2.111230),
[1455 iters] min = 13.69ms max = 13.79ms median = 13.76ms mean = 13.75msM1 firestorm @ 1 thread @ 3.0GHz w/ 2.6.2 conversion
INFO: Using CPU backend
INFO: Using num_threads == 1
The device support i8sdot:1, support fp16:1, support i8mm: 1
Creating MNN Interpreter: efficientformerv2_s0
(index: 985, score: 11.768960), (index: 644, score: 4.829375), (index: 108, score: 3.931292),
[1379 iters] min = 14.49ms max = 14.66ms median = 14.51ms mean = 14.51ms
Creating MNN Interpreter: efficientformerv2_s1
(index: 985, score: 13.083185), (index: 89, score: 4.154803), (index: 984, score: 4.072508),
[879 iters] min = 22.73ms max = 22.83ms median = 22.77ms mean = 22.78ms
Creating MNN Interpreter: efficientformerv2_s2
(index: 985, score: 12.495360), (index: 309, score: 3.706549), (index: 22, score: 3.682925),
[491 iters] min = 40.70ms max = 40.86ms median = 40.74ms mean = 40.74ms
Creating MNN Interpreter: SwiftFormer_XS
(index: 985, score: 11.912077), (index: 883, score: 4.997910), (index: 310, score: 4.615772),
[993 iters] min = 20.13ms max = 20.24ms median = 20.15ms mean = 20.15ms
Creating MNN Interpreter: SwiftFormer_S
(index: 985, score: 12.532909), (index: 89, score: 4.324093), (index: 720, score: 4.182640),
[668 iters] min = 29.91ms max = 30.07ms median = 29.95ms mean = 29.95ms
Creating MNN Interpreter: SwiftFormer_L1
(index: 985, score: 13.235222), (index: 309, score: 3.921143), (index: 310, score: 3.798431),
[440 iters] min = 45.53ms max = 45.67ms median = 45.54ms mean = 45.55ms
Creating MNN Interpreter: EMO_1M
(index: 985, score: 10.015739), (index: 309, score: 4.272019), (index: 310, score: 3.913734),
[1625 iters] min = 12.28ms max = 12.67ms median = 12.31ms mean = 12.32ms
Creating MNN Interpreter: EMO_2M
(index: 985, score: 9.377331), (index: 309, score: 3.261263), (index: 308, score: 3.011570),
[1072 iters] min = 18.61ms max = 18.79ms median = 18.66ms mean = 18.66ms
Creating MNN Interpreter: EMO_5M
(index: 985, score: 9.150205), (index: 883, score: 2.993564), (index: 308, score: 2.458643),
[611 iters] min = 32.74ms max = 33.51ms median = 32.76ms mean = 32.76ms
Creating MNN Interpreter: EMO_6M
(index: 985, score: 9.407994), (index: 883, score: 2.236737), (index: 309, score: 2.090058),
[574 iters] min = 34.81ms max = 35.00ms median = 34.88ms mean = 34.88ms
Creating MNN Interpreter: edgenext_xx_small
(index: 985, score: 10.881224), (index: 309, score: 4.952091), (index: 310, score: 4.636828),
[1920 iters] min = 10.40ms max = 10.56ms median = 10.42ms mean = 10.42ms
Creating MNN Interpreter: edgenext_x_small
(index: 985, score: 9.792659), (index: 309, score: 4.592906), (index: 308, score: 3.815865),
[1002 iters] min = 19.94ms max = 20.18ms median = 19.95ms mean = 19.96ms
Creating MNN Interpreter: edgenext_small
(index: 985, score: 12.166285), (index: 309, score: 4.538562), (index: 308, score: 4.057699),
[495 iters] min = 40.40ms max = 40.58ms median = 40.43ms mean = 40.44ms
Creating MNN Interpreter: mobilevitv2_050
(index: 985, score: 8.315649), (index: 309, score: 2.612311), (index: 584, score: 2.352622),
[1077 iters] min = 18.52ms max = 18.79ms median = 18.58ms mean = 18.58ms
Creating MNN Interpreter: mobilevitv2_075
(index: 985, score: 8.129767), (index: 309, score: 2.389330), (index: 308, score: 1.880279),
[573 iters] min = 34.90ms max = 35.27ms median = 34.95ms mean = 34.96ms
Creating MNN Interpreter: mobilevitv2_100
(index: 985, score: 8.256266), (index: 557, score: 2.220439), (index: 309, score: 1.944910),
[360 iters] min = 55.61ms max = 56.08ms median = 55.66ms mean = 55.67ms
Creating MNN Interpreter: mobilevitv2_125
(index: 985, score: 8.281974), (index: 309, score: 1.962234), (index: 883, score: 1.285449),
[249 iters] min = 80.32ms max = 80.94ms median = 80.37ms mean = 80.38ms
Creating MNN Interpreter: mobilevitv2_150
(index: 985, score: 9.098869), (index: 308, score: 2.259607), (index: 301, score: 2.159089),
[183 iters] min = 109.37ms max = 110.05ms median = 109.41ms mean = 109.44ms
Creating MNN Interpreter: mobilevitv2_175
(index: 985, score: 8.888629), (index: 494, score: 2.104702), (index: 309, score: 1.869344),
[141 iters] min = 142.69ms max = 143.23ms median = 142.74ms mean = 142.79ms
Creating MNN Interpreter: mobilevitv2_200
(index: 985, score: 8.531386), (index: 883, score: 2.248808), (index: 309, score: 2.237848),
[111 iters] min = 180.40ms max = 181.30ms median = 180.49ms mean = 180.56ms
Creating MNN Interpreter: mobilevit_xx_small
(index: 985, score: 12.652774), (index: 309, score: 6.357562), (index: 308, score: 6.236053),
[1121 iters] min = 17.83ms max = 18.02ms median = 17.85ms mean = 17.86ms
Creating MNN Interpreter: mobilevit_x_small
(index: 985, score: 12.998943), (index: 89, score: 6.411653), (index: 308, score: 5.775373),
[527 iters] min = 37.94ms max = 38.32ms median = 37.98ms mean = 37.99ms
Creating MNN Interpreter: mobilevit_small
(index: 985, score: 10.661409), (index: 838, score: 4.319293), (index: 309, score: 4.076161),
[333 iters] min = 60.08ms max = 60.62ms median = 60.12ms mean = 60.14ms
Creating MNN Interpreter: LeViT_128S
(index: 985, score: 11.427715), (index: 308, score: 3.451081), (index: 309, score: 3.319754),
[2279 iters] min = 8.75ms max = 8.85ms median = 8.78ms mean = 8.78ms
Creating MNN Interpreter: LeViT_128
(index: 985, score: 11.089683), (index: 309, score: 3.409015), (index: 113, score: 3.385430),
[1691 iters] min = 11.78ms max = 11.89ms median = 11.83ms mean = 11.83ms
Creating MNN Interpreter: LeViT_192
(index: 985, score: 11.594749), (index: 308, score: 3.186351), (index: 644, score: 3.177884),
[1156 iters] min = 17.28ms max = 17.42ms median = 17.31ms mean = 17.31ms
Creating MNN Interpreter: LeViT_256
(index: 985, score: 11.363626), (index: 108, score: 3.341140), (index: 310, score: 2.929424),
[700 iters] min = 28.57ms max = 28.81ms median = 28.60ms mean = 28.61ms
Creating MNN Interpreter: resnet50
(index: 985, score: 7.495943), (index: 113, score: -4.947984), (index: 310, score: -5.267875),
[271 iters] min = 73.86ms max = 74.83ms median = 73.92ms mean = 73.93ms
Creating MNN Interpreter: mobilenetv3_large_100
(index: 985, score: 9.592710), (index: 308, score: 2.354276), (index: 310, score: 2.337051),
[2352 iters] min = 8.49ms max = 8.56ms median = 8.50ms mean = 8.50ms
Creating MNN Interpreter: tf_efficientnetv2_b0
(index: 985, score: 9.555032), (index: 309, score: 2.378399), (index: 108, score: 2.289180),
[1096 iters] min = 18.23ms max = 18.42ms median = 18.26ms mean = 18.26ms
Creating MNN Interpreter: tf_efficientnetv2_b1
(index: 985, score: 9.484729), (index: 861, score: 2.258651), (index: 309, score: 2.134489),
[746 iters] min = 26.80ms max = 27.15ms median = 26.81ms mean = 26.82ms
Creating MNN Interpreter: tf_efficientnetv2_b2
(index: 985, score: 9.816973), (index: 883, score: 2.518728), (index: 113, score: 2.046238),
[517 iters] min = 38.70ms max = 39.17ms median = 38.73ms mean = 38.73ms
Creating MNN Interpreter: tf_efficientnetv2_b3
(index: 985, score: 9.089290), (index: 955, score: 2.892854), (index: 947, score: 2.188154),
[302 iters] min = 66.31ms max = 67.13ms median = 66.34ms mean = 66.36msM1 firestorm @ 1 thread @ 3.0GHz tinynn fp32
INFO: Using num_threads == 1
Creating tflite runtime interpreter: efficientformerv2_s0
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
(index: 985, score: 13.800571), (index: 644, score: 6.719913), (index: 662, score: 4.357441),
[1183 iters] min = 16.89ms max = 25.29ms median = 16.91ms mean = 16.92ms
Creating tflite runtime interpreter: efficientformerv2_s1
(index: 985, score: 11.002513), (index: 892, score: 5.913536), (index: 794, score: 5.847847),
[746 iters] min = 26.83ms max = 26.89ms median = 26.84ms mean = 26.84ms
Creating tflite runtime interpreter: efficientformerv2_s2
(index: 985, score: 14.202505), (index: 574, score: 5.209904), (index: 650, score: 4.950992),
[409 iters] min = 48.88ms max = 61.16ms median = 48.90ms mean = 48.93ms
Creating tflite runtime interpreter: SwiftFormer_XS
(index: 985, score: 16.348614), (index: 107, score: 8.219887), (index: 308, score: 7.765065),
[873 iters] min = 22.91ms max = 22.97ms median = 22.93ms mean = 22.93ms
Creating tflite runtime interpreter: SwiftFormer_S
(index: 985, score: 10.332458), (index: 309, score: 4.304403), (index: 507, score: 3.876854),
[568 iters] min = 35.21ms max = 35.28ms median = 35.22ms mean = 35.22ms
Creating tflite runtime interpreter: SwiftFormer_L1
(index: 985, score: 13.199683), (index: 310, score: 4.649525), (index: 309, score: 4.089820),
[362 iters] min = 55.36ms max = 55.44ms median = 55.38ms mean = 55.38ms
Creating tflite runtime interpreter: EMO_1M
(index: 985, score: 9.241662), (index: 328, score: 7.229661), (index: 619, score: 6.890592),
[1538 iters] min = 12.99ms max = 13.11ms median = 13.01ms mean = 13.01ms
Creating tflite runtime interpreter: EMO_2M
(index: 985, score: 9.554611), (index: 493, score: 6.152699), (index: 310, score: 3.873620),
[987 iters] min = 20.26ms max = 20.34ms median = 20.28ms mean = 20.28ms
Creating tflite runtime interpreter: EMO_5M
(index: 985, score: 8.625879), (index: 794, score: 5.527351), (index: 108, score: 4.698321),
[533 iters] min = 37.55ms max = 37.62ms median = 37.57ms mean = 37.57ms
Creating tflite runtime interpreter: EMO_6M
(index: 985, score: 9.466664), (index: 446, score: 5.847961), (index: 885, score: 4.761618),
[499 iters] min = 40.08ms max = 40.19ms median = 40.10ms mean = 40.10ms
Creating tflite runtime interpreter: edgenext_xx_small
(index: 144, score: 5.642828), (index: 858, score: 5.068350), (index: 132, score: 5.017061),
[1862 iters] min = 10.72ms max = 10.86ms median = 10.74ms mean = 10.74ms
Creating tflite runtime interpreter: edgenext_x_small
(index: 904, score: 9.758960), (index: 905, score: 8.679010), (index: 828, score: 7.538647),
[954 iters] min = 20.95ms max = 21.09ms median = 20.98ms mean = 20.98ms
Creating tflite runtime interpreter: edgenext_small
(index: 904, score: 6.315926), (index: 753, score: 5.907308), (index: 905, score: 5.188027),
[475 iters] min = 42.09ms max = 42.17ms median = 42.13ms mean = 42.13ms
Creating tflite runtime interpreter: mobilevitv2_050
(index: 905, score: 7.073186), (index: 688, score: 5.798759), (index: 530, score: 4.821216),
[1171 iters] min = 17.08ms max = 17.25ms median = 17.09ms mean = 17.09ms
Creating tflite runtime interpreter: mobilevitv2_075
(index: 904, score: 6.283208), (index: 753, score: 6.140928), (index: 905, score: 5.674746),
[612 iters] min = 32.66ms max = 32.72ms median = 32.68ms mean = 32.68ms
Creating tflite runtime interpreter: mobilevitv2_100
(index: 904, score: 6.422704), (index: 753, score: 4.768656), (index: 905, score: 3.757758),
[372 iters] min = 53.76ms max = 53.83ms median = 53.77ms mean = 53.77ms
Creating tflite runtime interpreter: mobilevitv2_125
(index: 549, score: 4.407668), (index: 905, score: 4.018123), (index: 753, score: 3.660830),
[252 iters] min = 79.62ms max = 79.71ms median = 79.64ms mean = 79.64ms
Creating tflite runtime interpreter: mobilevitv2_150
(index: 904, score: 6.959464), (index: 905, score: 5.279493), (index: 556, score: 4.383082),
[181 iters] min = 110.97ms max = 111.04ms median = 110.99ms mean = 110.99ms
Creating tflite runtime interpreter: mobilevitv2_175
(index: 905, score: 7.720383), (index: 904, score: 6.793842), (index: 753, score: 6.216552),
[137 iters] min = 146.93ms max = 147.09ms median = 146.95ms mean = 146.96ms
Creating tflite runtime interpreter: mobilevitv2_200
(index: 904, score: 7.456795), (index: 905, score: 5.135217), (index: 556, score: 4.294895),
[107 iters] min = 188.46ms max = 188.61ms median = 188.48ms mean = 188.49ms
Creating tflite runtime interpreter: mobilevit_xx_small
(index: 905, score: 9.074959), (index: 581, score: 7.366666), (index: 530, score: 6.987810),
[1478 iters] min = 13.52ms max = 13.61ms median = 13.53ms mean = 13.53ms
Creating tflite runtime interpreter: mobilevit_x_small
(index: 905, score: 8.960419), (index: 904, score: 7.904361), (index: 753, score: 6.592516),
[620 iters] min = 32.26ms max = 32.33ms median = 32.27ms mean = 32.27ms
Creating tflite runtime interpreter: mobilevit_small
(index: 904, score: 6.754923), (index: 905, score: 6.661246), (index: 858, score: 5.174719),
[356 iters] min = 56.26ms max = 56.34ms median = 56.28ms mean = 56.28ms
Creating tflite runtime interpreter: LeViT_128S
(index: 985, score: 8.677929), (index: 868, score: 8.497841), (index: 446, score: 7.851535),
[1894 iters] min = 10.55ms max = 10.58ms median = 10.56ms mean = 10.56ms
Creating tflite runtime interpreter: LeViT_128
(index: 985, score: 9.898071), (index: 619, score: 5.420589), (index: 539, score: 4.751773),
[1411 iters] min = 14.17ms max = 14.21ms median = 14.18ms mean = 14.18ms
Creating tflite runtime interpreter: LeViT_192
(index: 985, score: 10.134396), (index: 328, score: 7.682946), (index: 619, score: 6.307396),
[929 iters] min = 21.52ms max = 21.58ms median = 21.53ms mean = 21.53ms
Creating tflite runtime interpreter: LeViT_256
(index: 985, score: 8.615749), (index: 818, score: 6.255535), (index: 619, score: 6.188113),
[552 iters] min = 36.22ms max = 36.31ms median = 36.24ms mean = 36.24ms
Creating tflite runtime interpreter: resnet50
(index: 985, score: 7.842050), (index: 652, score: -3.425980), (index: 439, score: -4.438686),
[164 iters] min = 122.44ms max = 122.57ms median = 122.46ms mean = 122.47ms
Creating tflite runtime interpreter: mobilenetv3_large_100
(index: 112, score: 6.790292), (index: 985, score: 5.795063), (index: 591, score: 5.150109),
[2509 iters] min = 7.97ms max = 8.04ms median = 7.97ms mean = 7.97ms
tf_efficientnetv2_b0 model doesn't exist!!!
tf_efficientnetv2_b1 model doesn't exist!!!
tf_efficientnetv2_b2 model doesn't exist!!!
tf_efficientnetv2_b3 model doesn't exist!!!M1 firestorm @ 1 thread @ 3.0GHz tinynn int8 dynamic
INFO: Using num_threads == 1
Creating tflite runtime interpreter: efficientformerv2_s0
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
(index: 985, score: 10.414447), (index: 473, score: 7.323678), (index: 243, score: 5.948439),
[1785 iters] min = 11.19ms max = 11.44ms median = 11.21ms mean = 11.21ms
Creating tflite runtime interpreter: efficientformerv2_s1
(index: 985, score: 17.765656), (index: 89, score: 8.259610), (index: 512, score: 7.648966),
[1233 iters] min = 16.21ms max = 16.42ms median = 16.23ms mean = 16.23ms
Creating tflite runtime interpreter: efficientformerv2_s2
(index: 985, score: 10.992670), (index: 335, score: 6.229464), (index: 451, score: 6.014560),
[759 iters] min = 26.33ms max = 26.48ms median = 26.35ms mean = 26.36ms
Creating tflite runtime interpreter: SwiftFormer_XS
(index: 985, score: 12.597744), (index: 883, score: 6.429352), (index: 584, score: 6.104802),
[1469 iters] min = 13.60ms max = 13.82ms median = 13.62ms mean = 13.62ms
Creating tflite runtime interpreter: SwiftFormer_S
(index: 985, score: 13.702253), (index: 632, score: 7.228092), (index: 904, score: 5.929401),
[1135 iters] min = 17.61ms max = 17.74ms median = 17.63ms mean = 17.63ms
Creating tflite runtime interpreter: SwiftFormer_L1
(index: 985, score: 13.422840), (index: 904, score: 10.359863), (index: 556, score: 5.901742),
[823 iters] min = 24.30ms max = 24.39ms median = 24.32ms mean = 24.32ms
Creating tflite runtime interpreter: EMO_1M
(index: 985, score: 10.772147), (index: 310, score: 4.265167), (index: 309, score: 4.034026),
[1696 iters] min = 11.78ms max = 11.89ms median = 11.79ms mean = 11.79ms
Creating tflite runtime interpreter: EMO_2M
(index: 985, score: 9.784461), (index: 309, score: 3.289332), (index: 310, score: 3.090404),
[1233 iters] min = 16.21ms max = 16.32ms median = 16.22ms mean = 16.23ms
Creating tflite runtime interpreter: EMO_5M
(index: 985, score: 8.009010), (index: 310, score: 2.295238), (index: 311, score: 2.265032),
[820 iters] min = 24.38ms max = 24.47ms median = 24.40ms mean = 24.40ms
Creating tflite runtime interpreter: EMO_6M
(index: 985, score: 9.536195), (index: 883, score: 2.864874), (index: 968, score: 2.779290),
[783 iters] min = 25.53ms max = 25.58ms median = 25.55ms mean = 25.55ms
Creating tflite runtime interpreter: edgenext_xx_small
(index: 144, score: 5.726498), (index: 132, score: 5.228430), (index: 858, score: 4.910030),
[3219 iters] min = 6.21ms max = 6.30ms median = 6.22ms mean = 6.21ms
Creating tflite runtime interpreter: edgenext_x_small
(index: 905, score: 7.151570), (index: 904, score: 5.593434), (index: 539, score: 5.189555),
[1820 iters] min = 10.98ms max = 11.17ms median = 10.99ms mean = 10.99ms
Creating tflite runtime interpreter: edgenext_small
(index: 905, score: 7.833123), (index: 753, score: 5.813052), (index: 904, score: 4.813413),
[1087 iters] min = 18.39ms max = 18.57ms median = 18.40ms mean = 18.41ms
Creating tflite runtime interpreter: mobilevitv2_050
(index: 905, score: 6.351662), (index: 688, score: 6.100896), (index: 811, score: 5.178001),
[1517 iters] min = 13.17ms max = 24.71ms median = 13.18ms mean = 13.19ms
mobilevitv2_075 model doesn't exist!!!
mobilevitv2_100 model doesn't exist!!!
mobilevitv2_125 model doesn't exist!!!
Creating tflite runtime interpreter: mobilevitv2_150
(index: 885, score: 4.623362), (index: 905, score: 3.750659), (index: 148, score: 3.582521),
[404 iters] min = 49.55ms max = 49.69ms median = 49.57ms mean = 49.57ms
Creating tflite runtime interpreter: mobilevitv2_175
(index: 885, score: 5.663316), (index: 854, score: 5.009308), (index: 144, score: 4.522904),
[328 iters] min = 61.08ms max = 61.18ms median = 61.11ms mean = 61.11ms
Creating tflite runtime interpreter: mobilevitv2_200
(index: 905, score: 6.786233), (index: 650, score: 5.424253), (index: 904, score: 4.502124),
[272 iters] min = 73.70ms max = 73.83ms median = 73.73ms mean = 73.73ms
Creating tflite runtime interpreter: mobilevit_xx_small
(index: 898, score: 7.621788), (index: 611, score: 7.552627), (index: 782, score: 7.434354),
[1802 iters] min = 11.09ms max = 11.21ms median = 11.10ms mean = 11.10ms
Creating tflite runtime interpreter: mobilevit_x_small
(index: 905, score: 10.061533), (index: 904, score: 8.190913), (index: 818, score: 6.948528),
[874 iters] min = 22.79ms max = 22.94ms median = 22.89ms mean = 22.89ms
Creating tflite runtime interpreter: mobilevit_small
(index: 905, score: 8.444880), (index: 904, score: 3.538346), (index: 794, score: 3.359672),
[633 iters] min = 31.61ms max = 31.68ms median = 31.63ms mean = 31.63ms
Creating tflite runtime interpreter: LeViT_128S
(index: 985, score: 11.338488), (index: 744, score: 3.550334), (index: 309, score: 3.416615),
[3751 iters] min = 5.32ms max = 5.46ms median = 5.33ms mean = 5.33ms
Creating tflite runtime interpreter: LeViT_128
(index: 985, score: 11.076033), (index: 309, score: 3.194498), (index: 113, score: 3.029531),
[2761 iters] min = 7.23ms max = 7.35ms median = 7.24ms mean = 7.25ms
Creating tflite runtime interpreter: LeViT_192
(index: 985, score: 11.501357), (index: 326, score: 3.302804), (index: 644, score: 3.170937),
[2101 iters] min = 9.51ms max = 9.62ms median = 9.52ms mean = 9.52ms
Creating tflite runtime interpreter: LeViT_256
(index: 985, score: 11.314730), (index: 108, score: 3.116097), (index: 309, score: 3.107525),
[1431 iters] min = 13.95ms max = 14.02ms median = 13.98ms mean = 13.99ms
Creating tflite runtime interpreter: resnet50
(index: 985, score: 7.214152), (index: 310, score: -4.907701), (index: 113, score: -4.919555),
[608 iters] min = 32.87ms max = 32.96ms median = 32.91ms mean = 32.91ms
Creating tflite runtime interpreter: mobilenetv3_large_100
(index: 985, score: 9.674332), (index: 308, score: 2.503336), (index: 883, score: 2.451423),
[3183 iters] min = 6.28ms max = 6.36ms median = 6.28ms mean = 6.28ms
tf_efficientnetv2_b0 model doesn't exist!!!
tf_efficientnetv2_b1 model doesn't exist!!!
tf_efficientnetv2_b2 model doesn't exist!!!
tf_efficientnetv2_b3 model doesn't exist!!!M1 firestorm @ 1 thread @ 3.0GHz tinynn int8 ptq *fake*
INFO: Using num_threads == 1
Creating tflite runtime interpreter: efficientformerv2_s0
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
(index: 990, score: 15.554351), (index: 957, score: 15.554351), (index: 956, score: 15.554351),
[1976 iters] min = 10.11ms max = 10.25ms median = 10.12ms mean = 10.12ms
Creating tflite runtime interpreter: efficientformerv2_s1
(index: 910, score: 14.773200), (index: 892, score: 14.773200), (index: 641, score: 14.773200),
[1332 iters] min = 15.01ms max = 15.18ms median = 15.02ms mean = 15.02ms
Creating tflite runtime interpreter: efficientformerv2_s2
(index: 819, score: 13.587024), (index: 818, score: 13.587024), (index: 785, score: 12.763567),
[813 iters] min = 24.57ms max = 37.65ms median = 24.59ms mean = 24.60ms
SwiftFormer_XS model doesn't exist!!!
SwiftFormer_S model doesn't exist!!!
SwiftFormer_L1 model doesn't exist!!!
Creating tflite runtime interpreter: EMO_1M
(index: 985, score: 11.310929), (index: 108, score: 6.861964), (index: 310, score: 5.504652),
[2993 iters] min = 6.67ms max = 6.77ms median = 6.68ms mean = 6.68ms
Creating tflite runtime interpreter: EMO_2M
(index: 985, score: 9.640327), (index: 883, score: 3.666040), (index: 712, score: 3.530261),
[2033 iters] min = 9.83ms max = 10.04ms median = 9.84ms mean = 9.84ms
Creating tflite runtime interpreter: EMO_5M
(index: 985, score: 5.184752), (index: 506, score: 3.923597), (index: 644, score: 3.573275),
[1290 iters] min = 15.49ms max = 15.73ms median = 15.50ms mean = 15.50ms
Creating tflite runtime interpreter: EMO_6M
(index: 985, score: 6.443181), (index: 905, score: 3.692160), (index: 971, score: 3.474974),
[1203 iters] min = 16.61ms max = 16.81ms median = 16.63ms mean = 16.63ms
edgenext_xx_small model doesn't exist!!!
edgenext_x_small model doesn't exist!!!
edgenext_small model doesn't exist!!!
mobilevitv2_050 model doesn't exist!!!
mobilevitv2_075 model doesn't exist!!!
mobilevitv2_100 model doesn't exist!!!
mobilevitv2_125 model doesn't exist!!!
mobilevitv2_150 model doesn't exist!!!
mobilevitv2_175 model doesn't exist!!!
mobilevitv2_200 model doesn't exist!!!
mobilevit_xx_small model doesn't exist!!!
mobilevit_x_small model doesn't exist!!!
mobilevit_small model doesn't exist!!!
LeViT_128S model doesn't exist!!!
LeViT_128 model doesn't exist!!!
LeViT_192 model doesn't exist!!!
LeViT_256 model doesn't exist!!!
Creating tflite runtime interpreter: resnet50
(index: 985, score: 5.906343), (index: 310, score: -4.200066), (index: 308, score: -4.725074),
[737 iters] min = 27.16ms max = 27.23ms median = 27.17ms mean = 27.17ms
mobilenetv3_large_100 model doesn't exist!!!
tf_efficientnetv2_b0 model doesn't exist!!!
tf_efficientnetv2_b1 model doesn't exist!!!
tf_efficientnetv2_b2 model doesn't exist!!!
tf_efficientnetv2_b3 model doesn't exist!!!M1 firestorm @ 1 thread @ 3.0GHz onnx-tf fp32/fp16/bf16
INFO: Using num_threads == 1
Creating tflite runtime interpreter: efficientformerv2_s0
INFO: Created TensorFlow Lite delegate for select TF ops.
INFO: TfLiteFlexDelegate delegate: 35 nodes delegated out of 536 nodes with 35 partitions.
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
(index: 985, score: 11.767029), (index: 644, score: 4.848304), (index: 108, score: 3.925714),
[959 iters] min = 20.79ms max = 31.35ms median = 20.85ms mean = 20.86ms
Creating tflite runtime interpreter: efficientformerv2_s1
(index: 985, score: 13.112434), (index: 89, score: 4.162668), (index: 984, score: 4.077538),
[613 iters] min = 32.60ms max = 32.89ms median = 32.67ms mean = 32.67ms
Creating tflite runtime interpreter: efficientformerv2_s2
(index: 985, score: 12.485476), (index: 22, score: 3.693241), (index: 309, score: 3.691998),
[347 iters] min = 57.71ms max = 57.81ms median = 57.74ms mean = 57.74ms
Creating tflite runtime interpreter: SwiftFormer_XS
(index: 985, score: 11.914167), (index: 883, score: 5.001739), (index: 310, score: 4.622924),
[800 iters] min = 25.00ms max = 25.10ms median = 25.03ms mean = 25.03ms
Creating tflite runtime interpreter: SwiftFormer_S
(index: 985, score: 12.528475), (index: 89, score: 4.334188), (index: 720, score: 4.178122),
[528 iters] min = 37.86ms max = 38.06ms median = 37.90ms mean = 37.90ms
Creating tflite runtime interpreter: SwiftFormer_L1
(index: 985, score: 13.233636), (index: 309, score: 3.921285), (index: 310, score: 3.807555),
[338 iters] min = 59.12ms max = 59.82ms median = 59.19ms mean = 59.20ms
EMO_1M model doesn't exist!!!
EMO_2M model doesn't exist!!!
EMO_5M model doesn't exist!!!
EMO_6M model doesn't exist!!!
Creating tflite runtime interpreter: edgenext_xx_small
(index: 985, score: 10.885460), (index: 309, score: 4.954112), (index: 310, score: 4.638608),
[1129 iters] min = 17.68ms max = 17.76ms median = 17.71ms mean = 17.71ms
Creating tflite runtime interpreter: edgenext_x_small
(index: 985, score: 9.799910), (index: 309, score: 4.595185), (index: 308, score: 3.817009),
[613 iters] min = 32.64ms max = 32.74ms median = 32.67ms mean = 32.67ms
Creating tflite runtime interpreter: edgenext_small
(index: 985, score: 12.156300), (index: 309, score: 4.532577), (index: 308, score: 4.049805),
[318 iters] min = 62.88ms max = 63.04ms median = 62.94ms mean = 62.94ms
Creating tflite runtime interpreter: mobilevitv2_050
(index: 985, score: 8.315767), (index: 309, score: 2.612426), (index: 584, score: 2.352666),
[797 iters] min = 25.07ms max = 25.22ms median = 25.12ms mean = 25.12ms
Creating tflite runtime interpreter: mobilevitv2_075
(index: 985, score: 8.129782), (index: 309, score: 2.389380), (index: 308, score: 1.880313),
[421 iters] min = 47.45ms max = 58.20ms median = 47.50ms mean = 47.53ms
Creating tflite runtime interpreter: mobilevitv2_100
(index: 985, score: 8.256241), (index: 557, score: 2.220457), (index: 309, score: 1.944935),
[260 iters] min = 77.05ms max = 77.25ms median = 77.08ms mean = 77.08ms
Creating tflite runtime interpreter: mobilevitv2_125
(index: 985, score: 8.282048), (index: 309, score: 1.962256), (index: 883, score: 1.285460),
[178 iters] min = 112.48ms max = 112.76ms median = 112.54ms mean = 112.54ms
Creating tflite runtime interpreter: mobilevitv2_150
(index: 985, score: 9.099127), (index: 308, score: 2.259560), (index: 301, score: 2.159019),
[129 iters] min = 155.09ms max = 155.38ms median = 155.15ms mean = 155.15ms
Creating tflite runtime interpreter: mobilevitv2_175
(index: 985, score: 8.888693), (index: 494, score: 2.104596), (index: 309, score: 1.869223),
[98 iters] min = 204.19ms max = 204.46ms median = 204.23ms mean = 204.24ms
Creating tflite runtime interpreter: mobilevitv2_200
(index: 985, score: 8.531492), (index: 883, score: 2.249018), (index: 309, score: 2.237880),
[77 iters] min = 261.12ms max = 271.09ms median = 261.17ms mean = 261.30ms
Creating tflite runtime interpreter: mobilevit_xx_small
(index: 985, score: 12.652472), (index: 309, score: 6.357603), (index: 308, score: 6.236127),
[968 iters] min = 20.65ms max = 20.72ms median = 20.68ms mean = 20.68ms
Creating tflite runtime interpreter: mobilevit_x_small
(index: 985, score: 12.998844), (index: 89, score: 6.411969), (index: 308, score: 5.775460),
[393 iters] min = 50.88ms max = 51.10ms median = 50.91ms mean = 50.91ms
Creating tflite runtime interpreter: mobilevit_small
(index: 985, score: 10.661425), (index: 838, score: 4.319444), (index: 309, score: 4.076351),
[241 iters] min = 83.25ms max = 83.35ms median = 83.29ms mean = 83.29ms
Creating tflite runtime interpreter: LeViT_128S
(index: 985, score: 11.427817), (index: 308, score: 3.451130), (index: 309, score: 3.319763),
[1504 iters] min = 13.28ms max = 13.35ms median = 13.30ms mean = 13.30ms
Creating tflite runtime interpreter: LeViT_128
(index: 985, score: 11.089764), (index: 309, score: 3.409034), (index: 113, score: 3.385414),
[1143 iters] min = 17.46ms max = 27.34ms median = 17.50ms mean = 17.51ms
Creating tflite runtime interpreter: LeViT_192
(index: 985, score: 11.594851), (index: 308, score: 3.186352), (index: 644, score: 3.177924),
[759 iters] min = 26.32ms max = 26.41ms median = 26.36ms mean = 26.36ms
Creating tflite runtime interpreter: LeViT_256
(index: 985, score: 11.363824), (index: 108, score: 3.341186), (index: 310, score: 2.929489),
[448 iters] min = 44.62ms max = 57.70ms median = 44.64ms mean = 44.67ms
Creating tflite runtime interpreter: resnet50
(index: 985, score: 7.495987), (index: 113, score: -4.947908), (index: 310, score: -5.267951),
[161 iters] min = 124.29ms max = 124.40ms median = 124.33ms mean = 124.33ms
Creating tflite runtime interpreter: mobilenetv3_large_100
(index: 985, score: 9.592711), (index: 308, score: 2.354277), (index: 310, score: 2.337051),
[1907 iters] min = 10.48ms max = 10.61ms median = 10.49ms mean = 10.49ms
Creating tflite runtime interpreter: tf_efficientnetv2_b0
(index: 985, score: 9.554760), (index: 309, score: 2.378345), (index: 108, score: 2.289133),
[726 iters] min = 27.50ms max = 27.65ms median = 27.57ms mean = 27.57ms
Creating tflite runtime interpreter: tf_efficientnetv2_b1
(index: 985, score: 9.484579), (index: 861, score: 2.258523), (index: 309, score: 2.134490),
[466 iters] min = 42.93ms max = 43.06ms median = 42.98ms mean = 42.98ms
Creating tflite runtime interpreter: tf_efficientnetv2_b2
(index: 985, score: 9.816823), (index: 883, score: 2.518672), (index: 113, score: 2.046143),
[334 iters] min = 59.91ms max = 70.59ms median = 59.97ms mean = 60.00ms
Creating tflite runtime interpreter: tf_efficientnetv2_b3
(index: 985, score: 9.089396), (index: 955, score: 2.892823), (index: 947, score: 2.188147),
[198 iters] min = 101.34ms max = 101.56ms median = 101.40ms mean = 101.40msM1 firestorm @ 1 thread @ 3.0GHz onnx-tf dynamic int8
Creating tflite runtime interpreter: efficientformerv2_s0
INFO: Created TensorFlow Lite delegate for select TF ops.
INFO: TfLiteFlexDelegate delegate: 35 nodes delegated out of 536 nodes with 35 partitions.
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
(index: 985, score: 9.847589), (index: 473, score: 6.360453), (index: 623, score: 6.333659),
[1380 iters] min = 14.38ms max = 14.53ms median = 14.50ms mean = 14.50ms
Creating tflite runtime interpreter: efficientformerv2_s1
(index: 985, score: 12.012066), (index: 738, score: 7.356307), (index: 28, score: 6.549445),
[934 iters] min = 21.26ms max = 21.49ms median = 21.42ms mean = 21.42ms
Creating tflite runtime interpreter: efficientformerv2_s2
(index: 985, score: 12.407644), (index: 723, score: 5.381139), (index: 906, score: 4.645674),
[584 iters] min = 34.13ms max = 34.31ms median = 34.26ms mean = 34.26ms
Creating tflite runtime interpreter: SwiftFormer_XS
(index: 985, score: 12.037054), (index: 883, score: 5.053818), (index: 308, score: 4.783157),
[1359 iters] min = 14.63ms max = 15.00ms median = 14.72ms mean = 14.72ms
Creating tflite runtime interpreter: SwiftFormer_S
(index: 985, score: 13.849318), (index: 720, score: 5.001857), (index: 89, score: 4.420921),
[1039 iters] min = 19.14ms max = 19.33ms median = 19.26ms mean = 19.26ms
Creating tflite runtime interpreter: SwiftFormer_L1
(index: 985, score: 14.384110), (index: 309, score: 3.923572), (index: 310, score: 3.830448),
[744 iters] min = 26.82ms max = 26.99ms median = 26.88ms mean = 26.88ms
EMO_1M model doesn't exist!!!
EMO_2M model doesn't exist!!!
EMO_5M model doesn't exist!!!
EMO_6M model doesn't exist!!!
Creating tflite runtime interpreter: edgenext_xx_small
(index: 985, score: 10.747143), (index: 883, score: 4.571187), (index: 309, score: 4.446349),
[1342 iters] min = 14.88ms max = 14.96ms median = 14.91ms mean = 14.91ms
Creating tflite runtime interpreter: edgenext_x_small
(index: 985, score: 9.954880), (index: 309, score: 4.487042), (index: 308, score: 4.067084),
[777 iters] min = 25.75ms max = 25.82ms median = 25.77ms mean = 25.77ms
Creating tflite runtime interpreter: edgenext_small
(index: 985, score: 12.468886), (index: 309, score: 4.483033), (index: 308, score: 4.088212),
[478 iters] min = 41.86ms max = 42.02ms median = 41.90ms mean = 41.90ms
Creating tflite runtime interpreter: mobilevitv2_050
(index: 985, score: 8.055989), (index: 309, score: 2.675721), (index: 94, score: 2.217565),
[1071 iters] min = 18.60ms max = 18.73ms median = 18.67ms mean = 18.67ms
Creating tflite runtime interpreter: mobilevitv2_075
(index: 985, score: 8.002374), (index: 309, score: 2.393991), (index: 308, score: 1.741684),
[669 iters] min = 29.83ms max = 29.99ms median = 29.91ms mean = 29.91ms
Creating tflite runtime interpreter: mobilevitv2_100
(index: 985, score: 8.095229), (index: 557, score: 2.208805), (index: 73, score: 1.793792),
[468 iters] min = 42.70ms max = 55.76ms median = 42.73ms mean = 42.76ms
Creating tflite runtime interpreter: mobilevitv2_125
(index: 985, score: 8.135590), (index: 309, score: 2.029721), (index: 132, score: 1.361114),
[346 iters] min = 57.81ms max = 58.20ms median = 57.90ms mean = 57.90ms
Creating tflite runtime interpreter: mobilevitv2_150
(index: 985, score: 8.892390), (index: 308, score: 2.248269), (index: 309, score: 2.041805),
[271 iters] min = 73.78ms max = 74.17ms median = 73.82ms mean = 73.82ms
Creating tflite runtime interpreter: mobilevitv2_175
(index: 985, score: 9.095932), (index: 309, score: 2.302547), (index: 308, score: 1.961285),
[221 iters] min = 90.58ms max = 90.88ms median = 90.64ms mean = 90.65ms
Creating tflite runtime interpreter: mobilevitv2_200
(index: 985, score: 8.526460), (index: 883, score: 2.691330), (index: 309, score: 2.271109),
[182 iters] min = 109.84ms max = 110.26ms median = 109.90ms mean = 109.90ms
Creating tflite runtime interpreter: mobilevit_xx_small
(index: 985, score: 12.936302), (index: 309, score: 6.211125), (index: 308, score: 6.109789),
[1225 iters] min = 16.29ms max = 25.12ms median = 16.32ms mean = 16.33ms
Creating tflite runtime interpreter: mobilevit_x_small
(index: 985, score: 12.155066), (index: 951, score: 6.548617), (index: 89, score: 6.001575),
[528 iters] min = 37.88ms max = 38.06ms median = 37.92ms mean = 37.92ms
Creating tflite runtime interpreter: mobilevit_small
(index: 985, score: 10.645117), (index: 309, score: 4.341079), (index: 838, score: 4.252186),
[395 iters] min = 50.62ms max = 50.94ms median = 50.65ms mean = 50.66ms
Creating tflite runtime interpreter: LeViT_128S
(index: 985, score: 10.650167), (index: 644, score: 4.845832), (index: 949, score: 4.531433),
[3520 iters] min = 5.67ms max = 5.76ms median = 5.68ms mean = 5.68ms
Creating tflite runtime interpreter: LeViT_128
(index: 985, score: 10.408299), (index: 113, score: 3.053058), (index: 322, score: 2.965649),
[2553 iters] min = 7.82ms max = 7.96ms median = 7.83ms mean = 7.83ms
Creating tflite runtime interpreter: LeViT_192
(index: 985, score: 11.518022), (index: 326, score: 3.538946), (index: 949, score: 3.330873),
[1892 iters] min = 10.56ms max = 10.61ms median = 10.57ms mean = 10.57ms
Creating tflite runtime interpreter: LeViT_256
(index: 985, score: 11.887807), (index: 309, score: 3.870812), (index: 310, score: 3.431441),
[1258 iters] min = 15.87ms max = 16.00ms median = 15.91ms mean = 15.91ms
Creating tflite runtime interpreter: resnet50
(index: 985, score: 7.246205), (index: 310, score: -4.826327), (index: 113, score: -4.926934),
[585 iters] min = 34.17ms max = 34.26ms median = 34.20ms mean = 34.20ms
Creating tflite runtime interpreter: mobilenetv3_large_100
(index: 985, score: 9.637251), (index: 308, score: 2.416982), (index: 310, score: 2.384904),
[2507 iters] min = 7.94ms max = 8.05ms median = 7.98ms mean = 7.98ms
Creating tflite runtime interpreter: tf_efficientnetv2_b0
(index: 985, score: 9.417022), (index: 309, score: 2.254775), (index: 108, score: 2.241267),
[1336 iters] min = 14.96ms max = 15.07ms median = 14.98ms mean = 14.98ms
Creating tflite runtime interpreter: tf_efficientnetv2_b1
(index: 985, score: 9.583678), (index: 309, score: 2.332392), (index: 861, score: 2.236440),
[866 iters] min = 23.07ms max = 23.24ms median = 23.09ms mean = 23.10ms
Creating tflite runtime interpreter: tf_efficientnetv2_b2
(index: 985, score: 9.962457), (index: 883, score: 2.418920), (index: 108, score: 2.157927),
[633 iters] min = 31.59ms max = 31.72ms median = 31.62ms mean = 31.62ms
Creating tflite runtime interpreter: tf_efficientnetv2_b3
(index: 985, score: 8.798715), (index: 955, score: 2.787326), (index: 310, score: 2.172835),
[393 iters] min = 50.96ms max = 51.11ms median = 51.00ms mean = 51.00msM1 firestorm @ 1 thread @ 3.0GHz trace w/ onednn+acl by gcc-10
$ OMP_NUM_THREADS=1 MODEL=ALL make run-torch-perf
INFO: Using num_threads == 1
INFO: Using trace CPU backend
Creating pytorch module: efficientformerv2_s0
(index: 985, score: 11.767033), (index: 644, score: 4.848289), (index: 108, score: 3.925720),
[259 iters] min = 77.08ms max = 77.55ms median = 77.28ms mean = 77.27ms
Creating pytorch module: efficientformerv2_s1
(index: 985, score: 13.112442), (index: 89, score: 4.162664), (index: 984, score: 4.077511),
[173 iters] min = 115.70ms max = 116.05ms median = 115.90ms mean = 115.89ms
Creating pytorch module: efficientformerv2_s2
(index: 985, score: 12.485486), (index: 22, score: 3.693231), (index: 309, score: 3.692009),
[110 iters] min = 183.21ms max = 183.87ms median = 183.44ms mean = 183.44ms
Creating pytorch module: SwiftFormer_XS
(index: 985, score: 11.914165), (index: 883, score: 5.001728), (index: 310, score: 4.622917),
[302 iters] min = 66.11ms max = 66.60ms median = 66.36ms mean = 66.35ms
Creating pytorch module: SwiftFormer_S
(index: 985, score: 12.528474), (index: 89, score: 4.334189), (index: 720, score: 4.178121),
[225 iters] min = 88.79ms max = 89.29ms median = 89.03ms mean = 89.03ms
Creating pytorch module: SwiftFormer_L1
(index: 985, score: 13.233633), (index: 309, score: 3.921279), (index: 310, score: 3.807570),
[159 iters] min = 126.16ms max = 126.72ms median = 126.44ms mean = 126.42ms
Creating pytorch module: EMO_1M
(index: 985, score: 10.011185), (index: 309, score: 4.270287), (index: 310, score: 3.913450),
[425 iters] min = 47.01ms max = 47.21ms median = 47.07ms mean = 47.08ms
Creating pytorch module: EMO_2M
(index: 985, score: 9.367957), (index: 309, score: 3.259868), (index: 308, score: 3.008149),
[295 iters] min = 67.80ms max = 67.97ms median = 67.87ms mean = 67.88ms
Creating pytorch module: EMO_5M
(index: 985, score: 9.141463), (index: 883, score: 2.990551), (index: 308, score: 2.454388),
[184 iters] min = 109.11ms max = 109.46ms median = 109.28ms mean = 109.27ms
Creating pytorch module: EMO_6M
(index: 985, score: 9.396775), (index: 883, score: 2.240934), (index: 309, score: 2.083860),
[171 iters] min = 117.01ms max = 117.29ms median = 117.16ms mean = 117.16ms
Creating pytorch module: edgenext_xx_small
(index: 985, score: 10.885459), (index: 309, score: 4.954109), (index: 310, score: 4.638605),
[457 iters] min = 43.71ms max = 44.01ms median = 43.83ms mean = 43.84ms
Creating pytorch module: edgenext_x_small
(index: 985, score: 9.799909), (index: 309, score: 4.595184), (index: 308, score: 3.817010),
[237 iters] min = 84.26ms max = 84.90ms median = 84.58ms mean = 84.58ms
Creating pytorch module: edgenext_small
(index: 985, score: 12.156299), (index: 309, score: 4.532576), (index: 308, score: 4.049803),
[143 iters] min = 139.58ms max = 140.45ms median = 139.87ms mean = 139.90ms
Creating pytorch module: mobilevitv2_050
(index: 985, score: 8.315772), (index: 309, score: 2.612400), (index: 584, score: 2.352643),
[508 iters] min = 39.31ms max = 39.51ms median = 39.43ms mean = 39.42ms
Creating pytorch module: mobilevitv2_075
(index: 985, score: 8.129788), (index: 309, score: 2.389378), (index: 308, score: 1.880310),
[301 iters] min = 65.51ms max = 67.36ms median = 66.55ms mean = 66.51ms
Creating pytorch module: mobilevitv2_100
(index: 985, score: 8.256273), (index: 557, score: 2.220434), (index: 309, score: 1.944912),
[206 iters] min = 96.50ms max = 98.96ms median = 96.79ms mean = 97.34ms
Creating pytorch module: mobilevitv2_125
(index: 985, score: 8.281982), (index: 309, score: 1.962245), (index: 883, score: 1.285464),
[148 iters] min = 134.15ms max = 137.07ms median = 135.35ms mean = 135.51ms
Creating pytorch module: mobilevitv2_150
(index: 985, score: 9.098927), (index: 308, score: 2.259606), (index: 301, score: 2.159039),
[113 iters] min = 176.65ms max = 179.01ms median = 177.60ms mean = 177.64ms
Creating pytorch module: mobilevitv2_175
(index: 985, score: 8.888678), (index: 494, score: 2.104781), (index: 309, score: 1.869408),
[90 iters] min = 221.84ms max = 225.91ms median = 224.27ms mean = 224.02ms
Creating pytorch module: mobilevitv2_200
(index: 985, score: 8.531363), (index: 883, score: 2.248764), (index: 309, score: 2.237853),
[73 iters] min = 274.54ms max = 278.95ms median = 277.25ms mean = 277.15ms
Creating pytorch module: mobilevit_xx_small
(index: 985, score: 12.652477), (index: 309, score: 6.357602), (index: 308, score: 6.236127),
[483 iters] min = 41.33ms max = 41.56ms median = 41.42ms mean = 41.43ms
Creating pytorch module: mobilevit_x_small
(index: 985, score: 12.998842), (index: 89, score: 6.411968), (index: 308, score: 5.775460),
[223 iters] min = 89.92ms max = 90.22ms median = 90.07ms mean = 90.07ms
Creating pytorch module: mobilevit_small
(index: 985, score: 10.661425), (index: 838, score: 4.319453), (index: 309, score: 4.076357),
[158 iters] min = 126.86ms max = 127.35ms median = 127.02ms mean = 127.04ms
Creating pytorch module: LeViT_128S
(index: 985, score: 11.427817), (index: 308, score: 3.451130), (index: 309, score: 3.319760),
[1202 iters] min = 16.61ms max = 16.70ms median = 16.65ms mean = 16.65ms
Creating pytorch module: LeViT_128
(index: 985, score: 11.089767), (index: 309, score: 3.409031), (index: 113, score: 3.385418),
[931 iters] min = 21.43ms max = 21.57ms median = 21.50ms mean = 21.50ms
Creating pytorch module: LeViT_192
(index: 985, score: 11.594851), (index: 308, score: 3.186359), (index: 644, score: 3.177923),
[732 iters] min = 27.24ms max = 27.43ms median = 27.33ms mean = 27.33ms
Creating pytorch module: LeViT_256
(index: 985, score: 11.363824), (index: 108, score: 3.341188), (index: 310, score: 2.929487),
[430 iters] min = 46.43ms max = 46.67ms median = 46.52ms mean = 46.53ms
Creating pytorch module: resnet50
(index: 985, score: 7.495995), (index: 113, score: -4.947914), (index: 310, score: -5.267949),
[104 iters] min = 192.32ms max = 195.93ms median = 193.38ms mean = 193.51ms
Creating pytorch module: mobilenetv3_large_100
(index: 985, score: 9.592707), (index: 308, score: 2.354278), (index: 310, score: 2.337049),
[688 iters] min = 28.99ms max = 29.14ms median = 29.07ms mean = 29.07ms
Creating pytorch module: tf_efficientnetv2_b0
(index: 985, score: 9.554752), (index: 309, score: 2.378345), (index: 108, score: 2.289131),
[423 iters] min = 47.25ms max = 47.45ms median = 47.35ms mean = 47.35ms
Creating pytorch module: tf_efficientnetv2_b1
(index: 985, score: 9.484587), (index: 861, score: 2.258526), (index: 309, score: 2.134490),
[286 iters] min = 69.83ms max = 70.18ms median = 70.02ms mean = 70.02ms
Creating pytorch module: tf_efficientnetv2_b2
(index: 985, score: 9.816826), (index: 883, score: 2.518669), (index: 113, score: 2.046141),
[208 iters] min = 96.39ms max = 96.84ms median = 96.57ms mean = 96.57ms
Creating pytorch module: tf_efficientnetv2_b3
(index: 985, score: 9.089396), (index: 955, score: 2.892824), (index: 947, score: 2.188146),
[127 iters] min = 158.14ms max = 158.71ms median = 158.47ms mean = 158.46msM1 firestorm @ 1 thread @ 3.0GHz mobile w/ onednn+acl by gcc-10
$ OMP_NUM_THREADS=1 BACK=c MODEL=ALL make run-torch-perf
LD_LIBRARY_PATH=/home/loongson/zhouyingkun/torch-acl/lib:/home/loongson/zhouyingkun/arm_compute-v23.05-bin-linux-arm64-v8.2-a-neon/lib/arm64-v8.2-a-neon:/home/loongson/zhouyingkun/opencv/lib bin/torch-perf --only-test ALL --backend c
INFO: Using num_threads == 1
INFO: Using mobile CPU backend
Creating pytorch module: efficientformerv2_s0
(index: 985, score: 11.767030), (index: 644, score: 4.848297), (index: 108, score: 3.925721),
[318 iters] min = 62.79ms max = 63.43ms median = 63.00ms mean = 63.01ms
Creating pytorch module: efficientformerv2_s1
(index: 985, score: 13.112427), (index: 89, score: 4.162661), (index: 984, score: 4.077535),
[215 iters] min = 93.15ms max = 93.60ms median = 93.37ms mean = 93.38ms
Creating pytorch module: efficientformerv2_s2
(index: 985, score: 12.485474), (index: 22, score: 3.693241), (index: 309, score: 3.691998),
[134 iters] min = 149.02ms max = 149.66ms median = 149.33ms mean = 149.33ms
Creating pytorch module: SwiftFormer_XS
(index: 985, score: 11.914167), (index: 883, score: 5.001735), (index: 310, score: 4.622921),
[314 iters] min = 63.39ms max = 64.18ms median = 63.71ms mean = 63.71ms
Creating pytorch module: SwiftFormer_S
(index: 985, score: 12.528477), (index: 89, score: 4.334185), (index: 720, score: 4.178122),
[232 iters] min = 86.12ms max = 86.98ms median = 86.42ms mean = 86.43ms
Creating pytorch module: SwiftFormer_L1
(index: 985, score: 13.233639), (index: 309, score: 3.921288), (index: 310, score: 3.807554),
[163 iters] min = 122.60ms max = 123.64ms median = 123.05ms mean = 123.04ms
EMO_1M model doesn't exist!!!
EMO_2M model doesn't exist!!!
EMO_5M model doesn't exist!!!
EMO_6M model doesn't exist!!!
Creating pytorch module: edgenext_xx_small
(index: 985, score: 10.885463), (index: 309, score: 4.954110), (index: 310, score: 4.638607),
[550 iters] min = 36.29ms max = 36.70ms median = 36.40ms mean = 36.41ms
Creating pytorch module: edgenext_x_small
(index: 985, score: 9.799910), (index: 309, score: 4.595183), (index: 308, score: 3.817009),
[279 iters] min = 71.45ms max = 72.34ms median = 71.85ms mean = 71.83ms
Creating pytorch module: edgenext_small
(index: 985, score: 12.156300), (index: 309, score: 4.532576), (index: 308, score: 4.049804),
[162 iters] min = 123.72ms max = 124.86ms median = 124.17ms mean = 124.19ms
mobilevitv2_050 model doesn't exist!!!
mobilevitv2_075 model doesn't exist!!!
mobilevitv2_100 model doesn't exist!!!
mobilevitv2_125 model doesn't exist!!!
mobilevitv2_150 model doesn't exist!!!
mobilevitv2_175 model doesn't exist!!!
mobilevitv2_200 model doesn't exist!!!
mobilevit_xx_small model doesn't exist!!!
mobilevit_x_small model doesn't exist!!!
mobilevit_small model doesn't exist!!!
Creating pytorch module: LeViT_128S
(index: 985, score: 11.427816), (index: 308, score: 3.451128), (index: 309, score: 3.319762),
[1506 iters] min = 13.26ms max = 13.32ms median = 13.29ms mean = 13.29ms
Creating pytorch module: LeViT_128
(index: 985, score: 11.089766), (index: 309, score: 3.409033), (index: 113, score: 3.385415),
[1105 iters] min = 18.08ms max = 18.14ms median = 18.10ms mean = 18.10ms
Creating pytorch module: LeViT_192
(index: 985, score: 11.594851), (index: 308, score: 3.186354), (index: 644, score: 3.177923),
[818 iters] min = 24.44ms max = 24.51ms median = 24.47ms mean = 24.47ms
Creating pytorch module: LeViT_256
(index: 985, score: 11.363821), (index: 108, score: 3.341193), (index: 310, score: 2.929493),
[496 iters] min = 40.30ms max = 40.44ms median = 40.34ms mean = 40.34ms
resnet50 model doesn't exist!!!
Creating pytorch module: mobilenetv3_large_100
(index: 985, score: 9.592701), (index: 308, score: 2.354276), (index: 310, score: 2.337050),
[1434 iters] min = 13.93ms max = 14.01ms median = 13.95ms mean = 13.96ms
Creating pytorch module: tf_efficientnetv2_b0
(index: 985, score: 9.554751), (index: 309, score: 2.378344), (index: 108, score: 2.289130),
[561 iters] min = 35.64ms max = 35.81ms median = 35.68ms mean = 35.69ms
Creating pytorch module: tf_efficientnetv2_b1
(index: 985, score: 9.484585), (index: 861, score: 2.258525), (index: 309, score: 2.134489),
[351 iters] min = 56.97ms max = 57.12ms median = 57.01ms mean = 57.02ms
Creating pytorch module: tf_efficientnetv2_b2
(index: 985, score: 9.816826), (index: 883, score: 2.518668), (index: 113, score: 2.046140),
[256 iters] min = 78.21ms max = 78.52ms median = 78.35ms mean = 78.35ms
Creating pytorch module: tf_efficientnetv2_b3
(index: 985, score: 9.089395), (index: 955, score: 2.892825), (index: 947, score: 2.188146),
[151 iters] min = 132.57ms max = 132.95ms median = 132.72ms mean = 132.74msM1 firestorm @ 1 thread @ 3.0GHz trace w/ openblas by clang-14
$ OMP_NUM_THREADS=1 MODEL=ALL make run-torch-perf
LD_LIBRARY_PATH=/home/loongson/zhouyingkun/torch-blas/lib:/home/loongson/zhouyingkun/arm_compute-v23.05-bin-linux-arm64-v8.2-a-neon/lib/arm64-v8.2-a-neon:/home/loongson/zhouyingkun/opencv/lib bin/torch-perf --only-test ALL --backend z
INFO: Using num_threads == 1
INFO: Using trace CPU backend
Creating pytorch module: efficientformerv2_s0
(index: 985, score: 11.767040), (index: 644, score: 4.848290), (index: 108, score: 3.925722),
[373 iters] min = 53.55ms max = 53.76ms median = 53.66ms mean = 53.66ms
Creating pytorch module: efficientformerv2_s1
(index: 985, score: 13.112445), (index: 89, score: 4.162668), (index: 984, score: 4.077518),
[251 iters] min = 79.68ms max = 80.01ms median = 79.82ms mean = 79.82ms
Creating pytorch module: efficientformerv2_s2
(index: 985, score: 12.485485), (index: 22, score: 3.693229), (index: 309, score: 3.692007),
[149 iters] min = 134.05ms max = 134.67ms median = 134.44ms mean = 134.43ms
Creating pytorch module: SwiftFormer_XS
(index: 985, score: 11.914165), (index: 883, score: 5.001730), (index: 310, score: 4.622921),
[595 iters] min = 33.56ms max = 33.75ms median = 33.66ms mean = 33.66ms
Creating pytorch module: SwiftFormer_S
(index: 985, score: 12.528475), (index: 89, score: 4.334196), (index: 720, score: 4.178128),
[407 iters] min = 49.04ms max = 49.31ms median = 49.16ms mean = 49.16ms
Creating pytorch module: SwiftFormer_L1
(index: 985, score: 13.233629), (index: 309, score: 3.921285), (index: 310, score: 3.807566),
[274 iters] min = 73.17ms max = 73.37ms median = 73.25ms mean = 73.25ms
Creating pytorch module: EMO_1M
(index: 985, score: 10.011185), (index: 309, score: 4.270289), (index: 310, score: 3.913450),
[502 iters] min = 39.80ms max = 39.98ms median = 39.87ms mean = 39.87ms
Creating pytorch module: EMO_2M
(index: 985, score: 9.367955), (index: 309, score: 3.259869), (index: 308, score: 3.008149),
[357 iters] min = 55.98ms max = 56.23ms median = 56.10ms mean = 56.11ms
Creating pytorch module: EMO_5M
(index: 985, score: 9.141462), (index: 883, score: 2.990552), (index: 308, score: 2.454388),
[220 iters] min = 90.94ms max = 91.38ms median = 91.17ms mean = 91.17ms
Creating pytorch module: EMO_6M
(index: 985, score: 9.396772), (index: 883, score: 2.240934), (index: 309, score: 2.083858),
[206 iters] min = 97.36ms max = 97.70ms median = 97.51ms mean = 97.52ms
Creating pytorch module: edgenext_xx_small
(index: 985, score: 10.885461), (index: 309, score: 4.954110), (index: 310, score: 4.638609),
[978 iters] min = 20.38ms max = 20.56ms median = 20.45ms mean = 20.46ms
Creating pytorch module: edgenext_x_small
(index: 985, score: 9.799911), (index: 309, score: 4.595184), (index: 308, score: 3.817010),
[526 iters] min = 37.99ms max = 38.21ms median = 38.08ms mean = 38.08ms
Creating pytorch module: edgenext_small
(index: 985, score: 12.156297), (index: 309, score: 4.532577), (index: 308, score: 4.049804),
[294 iters] min = 67.89ms max = 68.23ms median = 68.04ms mean = 68.05ms
Creating pytorch module: mobilevitv2_050
(index: 985, score: 8.315772), (index: 309, score: 2.612400), (index: 584, score: 2.352643),
[556 iters] min = 35.92ms max = 36.20ms median = 36.02ms mean = 36.03ms
Creating pytorch module: mobilevitv2_075
(index: 985, score: 8.129786), (index: 309, score: 2.389378), (index: 308, score: 1.880310),
[325 iters] min = 60.64ms max = 62.37ms median = 61.57ms mean = 61.58ms
Creating pytorch module: mobilevitv2_100
(index: 985, score: 8.256277), (index: 557, score: 2.220437), (index: 309, score: 1.944912),
[219 iters] min = 90.74ms max = 92.47ms median = 91.54ms mean = 91.52ms
Creating pytorch module: mobilevitv2_125
(index: 985, score: 8.281981), (index: 309, score: 1.962245), (index: 883, score: 1.285464),
[159 iters] min = 124.66ms max = 126.84ms median = 125.97ms mean = 125.90ms
Creating pytorch module: mobilevitv2_150
(index: 985, score: 9.098921), (index: 308, score: 2.259605), (index: 301, score: 2.159040),
[121 iters] min = 165.11ms max = 167.51ms median = 166.24ms mean = 166.25ms
Creating pytorch module: mobilevitv2_175
(index: 985, score: 8.888677), (index: 494, score: 2.104781), (index: 309, score: 1.869408),
[95 iters] min = 210.20ms max = 212.14ms median = 210.85ms mean = 210.88ms
Creating pytorch module: mobilevitv2_200
(index: 985, score: 8.531368), (index: 883, score: 2.248762), (index: 309, score: 2.237853),
[77 iters] min = 260.56ms max = 263.32ms median = 261.57ms mean = 261.82ms
Creating pytorch module: mobilevit_xx_small
(index: 985, score: 12.652476), (index: 309, score: 6.357601), (index: 308, score: 6.236126),
[605 iters] min = 33.04ms max = 33.18ms median = 33.10ms mean = 33.11ms
Creating pytorch module: mobilevit_x_small
(index: 985, score: 12.998842), (index: 89, score: 6.411971), (index: 308, score: 5.775463),
[265 iters] min = 75.29ms max = 75.85ms median = 75.54ms mean = 75.54ms
Creating pytorch module: mobilevit_small
(index: 985, score: 10.661433), (index: 838, score: 4.319451), (index: 309, score: 4.076356),
[186 iters] min = 107.39ms max = 108.08ms median = 107.73ms mean = 107.72ms
Creating pytorch module: LeViT_128S
(index: 985, score: 11.427824), (index: 308, score: 3.451133), (index: 309, score: 3.319760),
[1516 iters] min = 13.15ms max = 13.23ms median = 13.19ms mean = 13.19ms
Creating pytorch module: LeViT_128
(index: 985, score: 11.089764), (index: 309, score: 3.409033), (index: 113, score: 3.385417),
[1160 iters] min = 17.17ms max = 17.29ms median = 17.25ms mean = 17.25ms
Creating pytorch module: LeViT_192
(index: 985, score: 11.594853), (index: 308, score: 3.186353), (index: 644, score: 3.177923),
[847 iters] min = 23.58ms max = 23.70ms median = 23.63ms mean = 23.63ms
Creating pytorch module: LeViT_256
(index: 985, score: 11.363823), (index: 108, score: 3.341187), (index: 310, score: 2.929486),
[536 iters] min = 37.32ms max = 37.42ms median = 37.36ms mean = 37.36ms
Creating pytorch module: resnet50
(index: 985, score: 7.495990), (index: 113, score: -4.947911), (index: 310, score: -5.267944),
[166 iters] min = 120.54ms max = 120.68ms median = 120.61ms mean = 120.61ms
Creating pytorch module: mobilenetv3_large_100
(index: 985, score: 9.592709), (index: 308, score: 2.354278), (index: 310, score: 2.337051),
[679 iters] min = 29.27ms max = 29.59ms median = 29.47ms mean = 29.47ms
Creating pytorch module: tf_efficientnetv2_b0
(index: 985, score: 9.554757), (index: 309, score: 2.378345), (index: 108, score: 2.289133),
[353 iters] min = 56.67ms max = 56.87ms median = 56.75ms mean = 56.75ms
Creating pytorch module: tf_efficientnetv2_b1
(index: 985, score: 9.484580), (index: 861, score: 2.258524), (index: 309, score: 2.134490),
[256 iters] min = 78.20ms max = 78.54ms median = 78.37ms mean = 78.37ms
Creating pytorch module: tf_efficientnetv2_b2
(index: 985, score: 9.816823), (index: 883, score: 2.518671), (index: 113, score: 2.046143),
[192 iters] min = 104.40ms max = 104.70ms median = 104.52ms mean = 104.53ms
Creating pytorch module: tf_efficientnetv2_b3
(index: 985, score: 9.089397), (index: 955, score: 2.892823), (index: 947, score: 2.188145),
[122 iters] min = 163.84ms max = 164.21ms median = 164.07ms mean = 164.07msM1 firestorm @ 1 thread @ 3.0GHz mobile w/ openblas by clang-14
$ OMP_NUM_THREADS=1 BACK=c MODEL=ALL make run-torch-perf
INFO: Using num_threads == 1
INFO: Using mobile CPU backend
Creating pytorch module: efficientformerv2_s0
(index: 985, score: 11.767038), (index: 644, score: 4.848302), (index: 108, score: 3.925722),
[692 iters] min = 28.85ms max = 28.99ms median = 28.92ms mean = 28.92ms
Creating pytorch module: efficientformerv2_s1
(index: 985, score: 13.112428), (index: 89, score: 4.162664), (index: 984, score: 4.077536),
[454 iters] min = 44.00ms max = 44.18ms median = 44.10ms mean = 44.10ms
Creating pytorch module: efficientformerv2_s2
(index: 985, score: 12.485475), (index: 22, score: 3.693243), (index: 309, score: 3.691999),
[266 iters] min = 75.14ms max = 75.39ms median = 75.27ms mean = 75.27ms
Creating pytorch module: SwiftFormer_XS
(index: 985, score: 11.914165), (index: 883, score: 5.001738), (index: 310, score: 4.622924),
[610 iters] min = 32.76ms max = 32.92ms median = 32.84ms mean = 32.84ms
Creating pytorch module: SwiftFormer_S
(index: 985, score: 12.528475), (index: 89, score: 4.334188), (index: 720, score: 4.178121),
[421 iters] min = 47.51ms max = 47.65ms median = 47.59ms mean = 47.58ms
Creating pytorch module: SwiftFormer_L1
(index: 985, score: 13.233637), (index: 309, score: 3.921288), (index: 310, score: 3.807558),
[279 iters] min = 71.72ms max = 71.89ms median = 71.81ms mean = 71.81ms
EMO_1M model doesn't exist!!!
EMO_2M model doesn't exist!!!
EMO_5M model doesn't exist!!!
EMO_6M model doesn't exist!!!
Creating pytorch module: edgenext_xx_small
(index: 985, score: 10.885464), (index: 309, score: 4.954108), (index: 310, score: 4.638605),
[1085 iters] min = 18.39ms max = 18.55ms median = 18.44ms mean = 18.44ms
Creating pytorch module: edgenext_x_small
(index: 985, score: 9.799910), (index: 309, score: 4.595186), (index: 308, score: 3.817011),
[569 iters] min = 35.15ms max = 35.25ms median = 35.20ms mean = 35.20ms
Creating pytorch module: edgenext_small
(index: 985, score: 12.156299), (index: 309, score: 4.532575), (index: 308, score: 4.049802),
[299 iters] min = 66.89ms max = 66.99ms median = 66.94ms mean = 66.94ms
mobilevitv2_050 model doesn't exist!!!
mobilevitv2_075 model doesn't exist!!!
mobilevitv2_100 model doesn't exist!!!
mobilevitv2_125 model doesn't exist!!!
mobilevitv2_150 model doesn't exist!!!
mobilevitv2_175 model doesn't exist!!!
mobilevitv2_200 model doesn't exist!!!
mobilevit_xx_small model doesn't exist!!!
mobilevit_x_small model doesn't exist!!!
mobilevit_small model doesn't exist!!!
Creating pytorch module: LeViT_128S
(index: 985, score: 11.427816), (index: 308, score: 3.451128), (index: 309, score: 3.319762),
[1678 iters] min = 11.90ms max = 11.96ms median = 11.92ms mean = 11.92ms
Creating pytorch module: LeViT_128
(index: 985, score: 11.089766), (index: 309, score: 3.409033), (index: 113, score: 3.385415),
[1244 iters] min = 16.07ms max = 16.12ms median = 16.09ms mean = 16.09ms
Creating pytorch module: LeViT_192
(index: 985, score: 11.594851), (index: 308, score: 3.186354), (index: 644, score: 3.177923),
[862 iters] min = 23.21ms max = 23.26ms median = 23.23ms mean = 23.23ms
Creating pytorch module: LeViT_256
(index: 985, score: 11.363821), (index: 108, score: 3.341193), (index: 310, score: 2.929493),
[518 iters] min = 38.59ms max = 38.67ms median = 38.63ms mean = 38.63ms
resnet50 model doesn't exist!!!
Creating pytorch module: mobilenetv3_large_100
(index: 985, score: 9.592703), (index: 308, score: 2.354278), (index: 310, score: 2.337049),
[1436 iters] min = 13.88ms max = 13.99ms median = 13.93ms mean = 13.93ms
Creating pytorch module: tf_efficientnetv2_b0
(index: 985, score: 9.554752), (index: 309, score: 2.378344), (index: 108, score: 2.289130),
[558 iters] min = 35.81ms max = 35.88ms median = 35.85ms mean = 35.85ms
Creating pytorch module: tf_efficientnetv2_b1
(index: 985, score: 9.484585), (index: 861, score: 2.258525), (index: 309, score: 2.134490),
[350 iters] min = 57.14ms max = 57.26ms median = 57.19ms mean = 57.18ms
Creating pytorch module: tf_efficientnetv2_b2
(index: 985, score: 9.816826), (index: 883, score: 2.518668), (index: 113, score: 2.046140),
[255 iters] min = 78.57ms max = 78.74ms median = 78.64ms mean = 78.65ms
Creating pytorch module: tf_efficientnetv2_b3
(index: 985, score: 9.089395), (index: 955, score: 2.892825), (index: 947, score: 2.188146),
[150 iters] min = 133.25ms max = 133.53ms median = 133.40ms mean = 133.39ms