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ncnn/benchmark/alexnet.param

alexnet.param 1.7 kB
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benchmark routine :)
8 years ago
update fused param files, enable ncnnoptimize tool build
7 years ago
shader shape specialization constant and basic local group size partition (#1523) * use Mat class for Shape description * shape specialization constant in compute shader * wip * wip * test forward_inplace, add binaryop unaryop sigmoid test * fix arm unaryop test * fix arm binaryop test * make shape hint optional, cast int8 to fp32, add cast test * wip * follow the good and old local size setting for conv1x1 * the optimal local size rewrite * fix build on msvc * add permute shader for all packing layout, add permute test * concat and slice patial shape constant, slice test * fix slice test * interp test * add lrn test, test packing layout implicitly * add eltwise test * add normalize test * add instancenorm test * reorg shape constant * simple local group size partition * add shape constant param
6 years ago
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  2. 15 15

  3. Input                    data                     0 1 data -23330=4,3,227,227,3 0=227 1=227 2=3

  4. Convolution              conv1                    1 1 data conv1_relu1 -23330=4,3,55,55,96 0=96 1=11 3=4 5=1 6=34848 9=1

  5. LRN                      norm1                    1 1 conv1_relu1 norm1 -23330=4,3,55,55,96 2=1.000000e-04

  6. Pooling                  pool1                    1 1 norm1 pool1 -23330=4,3,27,27,96 1=3 2=2

  7. ConvolutionDepthWise     conv2                    1 1 pool1 conv2_relu2 -23330=4,3,27,27,256 0=256 1=5 4=2 5=1 6=307200 7=2 9=1

  8. LRN                      norm2                    1 1 conv2_relu2 norm2 -23330=4,3,27,27,256 2=1.000000e-04

  9. Pooling                  pool2                    1 1 norm2 pool2 -23330=4,3,13,13,256 1=3 2=2

  10. Convolution              conv3                    1 1 pool2 conv3_relu3 -23330=4,3,13,13,384 0=384 1=3 4=1 5=1 6=884736 9=1

  11. ConvolutionDepthWise     conv4                    1 1 conv3_relu3 conv4_relu4 -23330=4,3,13,13,384 0=384 1=3 4=1 5=1 6=663552 7=2 9=1

  12. ConvolutionDepthWise     conv5                    1 1 conv4_relu4 conv5_relu5 -23330=4,3,13,13,256 0=256 1=3 4=1 5=1 6=442368 7=2 9=1

  13. Pooling                  pool5                    1 1 conv5_relu5 pool5 -23330=4,3,6,6,256 1=3 2=2

  14. InnerProduct             fc6                      1 1 pool5 fc6_drop6 -23330=4,1,4096,1,1 0=4096 1=1 2=37748736 9=1

  15. InnerProduct             fc7                      1 1 fc6_drop6 fc7_drop7 -23330=4,1,4096,1,1 0=4096 1=1 2=16777216 9=1

  16. InnerProduct             fc8                      1 1 fc7_drop7 fc8 -23330=4,1,1000,1,1 0=1000 1=1 2=4096000

  17. Softmax                  prob                     1 1 fc8 output -23330=4,1,1000,1,1

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