| | |
|---|
| | | cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.c); |
|---|
| | | cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.c); |
|---|
| | | } |
|---|
| | | /* |
|---|
| | | void forward_batchnorm_layer_gpu(layer l, network_state state) |
|---|
| | | { |
|---|
| | | if(l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); |
|---|
| | | if(l.type == CONNECTED){ |
|---|
| | | l.out_c = l.outputs; |
|---|
| | | l.out_h = l.out_w = 1; |
|---|
| | | } |
|---|
| | | if (state.train) { |
|---|
| | | fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); |
|---|
| | | fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu); |
|---|
| | | |
|---|
| | | scal_ongpu(l.out_c, .99, l.rolling_mean_gpu, 1); |
|---|
| | | axpy_ongpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1); |
|---|
| | | scal_ongpu(l.out_c, .99, l.rolling_variance_gpu, 1); |
|---|
| | | axpy_ongpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1); |
|---|
| | | |
|---|
| | | copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); |
|---|
| | | normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); |
|---|
| | | copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); |
|---|
| | | } else { |
|---|
| | | normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); |
|---|
| | | } |
|---|
| | | |
|---|
| | | scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); |
|---|
| | | } |
|---|
| | | |
|---|
| | | void backward_batchnorm_layer_gpu(const layer l, network_state state) |
|---|
| | | { |
|---|
| | | backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu); |
|---|
| | | |
|---|
| | | scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); |
|---|
| | | |
|---|
| | | fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta_gpu); |
|---|
| | | fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu); |
|---|
| | | normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); |
|---|
| | | if(l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); |
|---|
| | | } |
|---|
| | | #endif |
|---|
| | | */ |
|---|
| | | |
|---|
| | | |
|---|
| | | void forward_batchnorm_layer_gpu(layer l, network_state state) |
|---|
| | | { |
|---|
