layer filters size input output 0 conv 64 7 x 7 / 2 256 x 256 x 3 -> 128 x 128 x 64 0.308 BFLOPs 1 max 2 x 2 / 2 128 x 128 x 64 -> 64 x 64 x 64 2 conv 64 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.034 BFLOPs 3 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 4 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 5 res 1 64 x 64 x 64 -> 64 x 64 x 256 6 conv 64 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 64 0.134 BFLOPs 7 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 8 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 9 res 5 64 x 64 x 256 -> 64 x 64 x 256 10 conv 64 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 64 0.134 BFLOPs 11 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 12 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 13 res 9 64 x 64 x 256 -> 64 x 64 x 256 14 conv 128 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 128 0.268 BFLOPs 15 conv 128 3 x 3 / 2 64 x 64 x 128 -> 32 x 32 x 128 0.302 BFLOPs 16 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 17 res 13 64 x 64 x 256 -> 32 x 32 x 512 18 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 19 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 20 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 21 res 17 32 x 32 x 512 -> 32 x 32 x 512 22 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 23 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 24 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 25 res 21 32 x 32 x 512 -> 32 x 32 x 512 26 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 27 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 28 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 29 res 25 32 x 32 x 512 -> 32 x 32 x 512 30 conv 256 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BFLOPs 31 conv 256 3 x 3 / 2 32 x 32 x 256 -> 16 x 16 x 256 0.302 BFLOPs 32 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 33 res 29 32 x 32 x 512 -> 16 x 16 x1024 34 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 35 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 36 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 37 res 33 16 x 16 x1024 -> 16 x 16 x1024 38 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 39 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 40 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 41 res 37 16 x 16 x1024 -> 16 x 16 x1024 42 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 43 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 44 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 45 res 41 16 x 16 x1024 -> 16 x 16 x1024 46 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 47 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 48 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 49 res 45 16 x 16 x1024 -> 16 x 16 x1024 50 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 51 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 52 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 53 res 49 16 x 16 x1024 -> 16 x 16 x1024 54 conv 512 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 512 0.268 BFLOPs 55 conv 512 3 x 3 / 2 16 x 16 x 512 -> 8 x 8 x 512 0.302 BFLOPs 56 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 0.134 BFLOPs 57 res 53 16 x 16 x1024 -> 8 x 8 x2048 58 conv 512 1 x 1 / 1 8 x 8 x2048 -> 8 x 8 x 512 0.134 BFLOPs 59 conv 512 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x 512 0.302 BFLOPs 60 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 0.134 BFLOPs 61 res 57 8 x 8 x2048 -> 8 x 8 x2048 62 conv 512 1 x 1 / 1 8 x 8 x2048 -> 8 x 8 x 512 0.134 BFLOPs 63 conv 512 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x 512 0.302 BFLOPs 64 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 0.134 BFLOPs 65 res 61 8 x 8 x2048 -> 8 x 8 x2048 66 avg 8 x 8 x2048 -> 2048 67 conv 1000 1 x 1 / 1 1 x 1 x2048 -> 1 x 1 x1000 0.004 BFLOPs 68 softmax 1000 Loading weights from ../../../../../data/darknet/resnet50.weights...Done! ../data/dog.jpg: Predicted in 0.016876 seconds. layer filters size input output 0 conv 64 7 x 7 / 2 256 x 256 x 3 -> 128 x 128 x 64 0.308 BFLOPs 1 max 2 x 2 / 2 128 x 128 x 64 -> 64 x 64 x 64 2 conv 64 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.034 BFLOPs 3 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 4 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 5 res 1 64 x 64 x 64 -> 64 x 64 x 256 6 conv 64 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 64 0.134 BFLOPs 7 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 8 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 9 res 5 64 x 64 x 256 -> 64 x 64 x 256 10 conv 64 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 64 0.134 BFLOPs 11 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 12 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 13 res 9 64 x 64 x 256 -> 64 x 64 x 256 14 conv 128 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 128 0.268 BFLOPs 15 conv 128 3 x 3 / 2 64 x 64 x 128 -> 32 x 32 x 128 0.302 BFLOPs 16 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 17 res 13 64 x 64 x 256 -> 32 x 32 x 512 18 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 19 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 20 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 21 res 17 32 x 32 x 512 -> 32 x 32 x 512 22 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 23 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 24 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 25 res 21 32 x 32 x 512 -> 32 x 32 x 512 26 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 27 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 28 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 29 res 25 32 x 32 x 512 -> 32 x 32 x 512 30 conv 256 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BFLOPs 31 conv 256 3 x 3 / 2 32 x 32 x 256 -> 16 x 16 x 256 0.302 BFLOPs 32 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 33 res 29 32 x 32 x 512 -> 16 x 16 x1024 34 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 35 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 36 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 37 res 33 16 x 16 x1024 -> 16 x 16 x1024 38 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 39 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 40 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 41 res 37 16 x 16 x1024 -> 16 x 16 x1024 42 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 43 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 44 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 45 res 41 16 x 16 x1024 -> 16 x 16 x1024 46 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 47 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 48 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 49 res 45 16 x 16 x1024 -> 16 x 16 x1024 50 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 51 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 52 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 53 res 49 16 x 16 x1024 -> 16 x 16 x1024 54 conv 512 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 512 0.268 BFLOPs 55 conv 512 3 x 3 / 2 16 x 16 x 512 -> 8 x 8 x 512 0.302 BFLOPs 56 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 0.134 BFLOPs 57 res 53 16 x 16 x1024 -> 8 x 8 x2048 58 conv 512 1 x 1 / 1 8 x 8 x2048 -> 8 x 8 x 512 0.134 BFLOPs 59 conv 512 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x 512 0.302 BFLOPs 60 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 0.134 BFLOPs 61 res 57 8 x 8 x2048 -> 8 x 8 x2048 62 conv 512 1 x 1 / 1 8 x 8 x2048 -> 8 x 8 x 512 0.134 BFLOPs 63 conv 512 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x 512 0.302 BFLOPs 64 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 0.134 BFLOPs 65 res 61 8 x 8 x2048 -> 8 x 8 x2048 66 avg 8 x 8 x2048 -> 2048 67 conv 1000 1 x 1 / 1 1 x 1 x2048 -> 1 x 1 x1000 0.004 BFLOPs 68 softmax 1000 Loading weights from ../../../../../data/darknet/resnet50.weights...Done! ../data/dog.jpg: Predicted in 0.017422 seconds. layer filters size input output 0 conv 64 7 x 7 / 2 256 x 256 x 3 -> 128 x 128 x 64 0.308 BFLOPs 1 max 2 x 2 / 2 128 x 128 x 64 -> 64 x 64 x 64 2 conv 64 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.034 BFLOPs 3 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 4 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 5 res 1 64 x 64 x 64 -> 64 x 64 x 256 6 conv 64 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 64 0.134 BFLOPs 7 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 8 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 9 res 5 64 x 64 x 256 -> 64 x 64 x 256 10 conv 64 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 64 0.134 BFLOPs 11 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 12 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 13 res 9 64 x 64 x 256 -> 64 x 64 x 256 14 conv 128 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 128 0.268 BFLOPs 15 conv 128 3 x 3 / 2 64 x 64 x 128 -> 32 x 32 x 128 0.302 BFLOPs 16 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 17 res 13 64 x 64 x 256 -> 32 x 32 x 512 18 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 19 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 20 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 21 res 17 32 x 32 x 512 -> 32 x 32 x 512 22 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 23 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 24 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 25 res 21 32 x 32 x 512 -> 32 x 32 x 512 26 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 27 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 28 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 29 res 25 32 x 32 x 512 -> 32 x 32 x 512 30 conv 256 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BFLOPs 31 conv 256 3 x 3 / 2 32 x 32 x 256 -> 16 x 16 x 256 0.302 BFLOPs 32 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 33 res 29 32 x 32 x 512 -> 16 x 16 x1024 34 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 35 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 36 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 37 res 33 16 x 16 x1024 -> 16 x 16 x1024 38 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 39 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 40 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 41 res 37 16 x 16 x1024 -> 16 x 16 x1024 42 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 43 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 44 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 45 res 41 16 x 16 x1024 -> 16 x 16 x1024 46 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 47 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 48 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 49 res 45 16 x 16 x1024 -> 16 x 16 x1024 50 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 51 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 52 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 53 res 49 16 x 16 x1024 -> 16 x 16 x1024 54 conv 512 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 512 0.268 BFLOPs 55 conv 512 3 x 3 / 2 16 x 16 x 512 -> 8 x 8 x 512 0.302 BFLOPs 56 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 0.134 BFLOPs 57 res 53 16 x 16 x1024 -> 8 x 8 x2048 58 conv 512 1 x 1 / 1 8 x 8 x2048 -> 8 x 8 x 512 0.134 BFLOPs 59 conv 512 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x 512 0.302 BFLOPs 60 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 0.134 BFLOPs 61 res 57 8 x 8 x2048 -> 8 x 8 x2048 62 conv 512 1 x 1 / 1 8 x 8 x2048 -> 8 x 8 x 512 0.134 BFLOPs 63 conv 512 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x 512 0.302 BFLOPs 64 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 0.134 BFLOPs 65 res 61 8 x 8 x2048 -> 8 x 8 x2048 66 avg 8 x 8 x2048 -> 2048 67 conv 1000 1 x 1 / 1 1 x 1 x2048 -> 1 x 1 x1000 0.004 BFLOPs 68 softmax 1000 Loading weights from ../../../../../data/darknet/resnet50.weights...Done! layer filters size input output 0 conv 64 7 x 7 / 2 256 x 256 x 3 -> 128 x 128 x 64 0.308 BFLOPs 1 max 2 x 2 / 2 128 x 128 x 64 -> 64 x 64 x 64 2 conv 64 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.034 BFLOPs 3 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 4 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 5 res 1 64 x 64 x 64 -> 64 x 64 x 256 6 conv 64 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 64 0.134 BFLOPs 7 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 8 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 9 res 5 64 x 64 x 256 -> 64 x 64 x 256 10 conv 64 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 64 0.134 BFLOPs 11 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64 0.302 BFLOPs 12 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256 0.134 BFLOPs 13 res 9 64 x 64 x 256 -> 64 x 64 x 256 14 conv 128 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 128 0.268 BFLOPs 15 conv 128 3 x 3 / 2 64 x 64 x 128 -> 32 x 32 x 128 0.302 BFLOPs 16 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 17 res 13 64 x 64 x 256 -> 32 x 32 x 512 18 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 19 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 20 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 21 res 17 32 x 32 x 512 -> 32 x 32 x 512 22 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 23 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 24 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 25 res 21 32 x 32 x 512 -> 32 x 32 x 512 26 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128 0.134 BFLOPs 27 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128 0.302 BFLOPs 28 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512 0.134 BFLOPs 29 res 25 32 x 32 x 512 -> 32 x 32 x 512 30 conv 256 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 256 0.268 BFLOPs 31 conv 256 3 x 3 / 2 32 x 32 x 256 -> 16 x 16 x 256 0.302 BFLOPs 32 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024 0.134 BFLOPs 33 res 29 32 x 32 x 512 -> 16 x 16 x1024 34 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256 0.134 BFLOPs 35 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256 0.302 BFLOPs 36 Traceback (most recent call last): File "run_real_all.py", line 646, in main() File "run_real_all.py", line 638, in main result_dict = process_results(workload_dict, iterations) File "run_real_all.py", line 187, in process_results result_dict[para][workload][config].append(process_file(log_file, config)) File "run_real_all.py", line 155, in process_file result_dict['allocation'] += int(words[3]) IndexError: list index out of range