| 975 | // ---------------------------------------------------------------------------------------- |
| 976 | |
| 977 | void batch_normalize_conv_inference ( |
| 978 | const double eps, |
| 979 | resizable_tensor& dest, |
| 980 | const tensor& src, |
| 981 | const tensor& gamma, |
| 982 | const tensor& beta, |
| 983 | const tensor& running_means, |
| 984 | const tensor& running_variances |
| 985 | ) |
| 986 | { |
| 987 | DLIB_CASSERT( |
| 988 | gamma.num_samples() == 1 && |
| 989 | gamma.nr() == 1 && |
| 990 | gamma.nc() == 1 && |
| 991 | gamma.k() == src.k() && |
| 992 | have_same_dimensions(gamma, beta) && |
| 993 | have_same_dimensions(gamma, running_means) && |
| 994 | have_same_dimensions(gamma, running_variances) && |
| 995 | eps > 0, |
| 996 | "\ngamma.num_samples(): " << gamma.num_samples() << |
| 997 | "\ngamma.k(): " << gamma.k() << |
| 998 | "\ngamma.nr(): " << gamma.nr() << |
| 999 | "\ngamma.nc(): " << gamma.nc() << |
| 1000 | "\nbeta.num_samples(): " << beta.num_samples() << |
| 1001 | "\nbeta.k(): " << beta.k() << |
| 1002 | "\nbeta.nr(): " << beta.nr() << |
| 1003 | "\nbeta.nc(): " << beta.nc() << |
| 1004 | "\nrunning_means.num_samples(): " << running_means.num_samples() << |
| 1005 | "\nrunning_means.k(): " << running_means.k() << |
| 1006 | "\nrunning_means.nr(): " << running_means.nr() << |
| 1007 | "\nrunning_means.nc(): " << running_means.nc() << |
| 1008 | "\nrunning_variances.num_samples(): " << running_variances.num_samples() << |
| 1009 | "\nrunning_variances.k(): " << running_variances.k() << |
| 1010 | "\nrunning_variances.nr(): " << running_variances.nr() << |
| 1011 | "\nrunning_variances.nc(): " << running_variances.nc() << |
| 1012 | "\nsrc.k(): " << src.k() << |
| 1013 | "\nsrc.nr(): " << src.nr() << |
| 1014 | "\nsrc.nc(): " << src.nc() << |
| 1015 | "\neps: " << eps |
| 1016 | ); |
| 1017 | dest.copy_size(src); |
| 1018 | |
| 1019 | auto d = dest.host(); |
| 1020 | auto s = src.host(); |
| 1021 | auto g = gamma.host(); |
| 1022 | auto b = beta.host(); |
| 1023 | auto m = running_means.host(); |
| 1024 | auto v = running_variances.host(); |
| 1025 | |
| 1026 | const long num = src.nr()*src.nc(); |
| 1027 | for (long n = 0; n < src.num_samples(); ++n) |
| 1028 | { |
| 1029 | for (long k = 0; k < src.k(); ++k) |
| 1030 | { |
| 1031 | const float invstd = 1.0f/std::sqrt(v[k] + eps); |
| 1032 | for (long j = 0; j < num; ++j) |
| 1033 | { |
| 1034 | *d = g[k]*(*s - m[k])*invstd + b[k]; |