| | |
| | | |
| | | layer.rolling_mean = calloc(c, sizeof(float)); |
| | | layer.rolling_variance = calloc(c, sizeof(float)); |
| | | |
| | | layer.forward = forward_batchnorm_layer; |
| | | layer.backward = backward_batchnorm_layer; |
| | | #ifdef GPU |
| | | layer.forward_gpu = forward_batchnorm_layer_gpu; |
| | | layer.backward_gpu = backward_batchnorm_layer_gpu; |
| | | |
| | | layer.output_gpu = cuda_make_array(layer.output, h * w * c * batch); |
| | | layer.delta_gpu = cuda_make_array(layer.delta, h * w * c * batch); |
| | | |
| | |
| | | } |
| | | |
| | | #ifdef GPU |
| | | |
| | | void pull_batchnorm_layer(layer l) |
| | | { |
| | | cuda_pull_array(l.scales_gpu, l.scales, l.c); |
| | | cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.c); |
| | | cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.c); |
| | | } |
| | | void push_batchnorm_layer(layer l) |
| | | { |
| | | cuda_push_array(l.scales_gpu, l.scales, l.c); |
| | | 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); |